research-article

Vision-based topological mapping and localization methods

Authors:

Emilio Garcia-Fidalgo,

Alberto OrtizAuthors Info & Claims

Robotics and Autonomous Systems, Volume 64, Issue C

Pages 1 - 20

https://doi.org/10.1016/j.robot.2014.11.009

Published: 01 February 2015 Publication History

Abstract

Topological maps model the environment as a graph, where nodes are distinctive places of the environment and edges indicate topological relationships between them. They represent an interesting alternative to the classic metric maps, due to their simplicity and storage needs, what has made topological mapping and localization an active research area. The different solutions that have been proposed during years have been designed around several kinds of sensors. However, in the last decades, vision approaches have emerged because of the technology improvements and the amount of useful information that a camera can provide. In this paper, we review the main solutions presented in the last fifteen years, and classify them in accordance to the kind of image descriptor employed. Advantages and disadvantages of each approach are thoroughly reviewed and discussed. A comprehensive survey on vision-based topological mapping and localization methods.Classification according to image descriptions: global, local, BoW and combinations.Advantages and disadvantages of each approach are discussed.

References

[1]

B. Kuipers, Y.-T. Byun, A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations, Robot. Auton. Syst., 8 (1991) 47-63.

[2]

H. Choset, K. Nagatani, Topological simultaneous localization and mapping (SLAM): Toward exact localization without explicit localization, IEEE Trans. Robot. Autom., 17 (2001) 125-137.

[3]

E. Remolina, B. Kuipers, Towards a general theory of topological maps, Artif. Intell., 152 (2004) 47-104.

Digital Library

[4]

H. Durrant-Whyte, T. Bailey, Simultaneous localisation and mapping (SLAM): Part I the essential algorithms, IEEE Robot. Autom. Mag., 2 (2006) 99-110.

[5]

F. Bonin-Font, A. Ortiz, G. Oliver, Visual navigation for mobile robots: A survey, J. Intell. Robot. Syst., 53 (2008) 263-296.

Digital Library

[6]

J. Fuentes-Pacheco, J. Ruiz-Ascencio, J.M. Rendón-Mancha, Visual simultaneous localization and mapping: A survey, Artif. Intell. Rev. (2012).

[7]

E. Olson, J. Leonard, S. Teller, Fast iterative alignment of pose graphs with poor initial estimates, in: IEEE International Conference on Robotics and Automation, 2006, pp. 2262-2269.

[8]

U. Frese, L. Schroder, Closing a million-landmarks loop, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006, pp. 5032-5039. http://dx.doi.org/10.1109/IROS.2006.282531.

[9]

F. Dellaert, M. Kaess, Square root SAM: Simultaneous localization and mapping via square root information smoothing, Internat. J. Robot. Res., 25 (2006) 1181-1203.

Digital Library

[10]

M. Kaess, A. Ranganathan, F. Dellaert, iSAM: Incremental smoothing and mapping, IEEE Trans. Robot., 24 (2008) 1365-1378.

Digital Library

[11]

G. Grisetti, C. Stachniss, W. Burgard, Nonlinear constraint network optimization for efficient map learning, IEEE Trans. Intell. Transp. Syst., 10 (2009) 428-439.

Digital Library

[12]

K. Konolige, G. Grisetti, R. Kummerle, W. Burgard, B. Limketkai, R. Vincent, Efficient sparse pose adjustment for 2D mapping, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010, pp. 22-29. http://dx.doi.org/10.1109/IROS.2010.5649043.

[13]

M. Kaess, H. Johannsson, R. Roberts, V. Ila, J. Leonard, F. Dellaert, iSAM2: Incremental smoothing and mapping with fluid relinearization and incremental variable reordering, in: IEEE International Conference on Robotics and Automation, 2011, pp. 3281-3288. http://dx.doi.org/10.1109/ICRA.2011.5979641.

[14]

R. Kummerle, G. Grisetti, H. Strasdat, K. Konolige, W. Burgard, g2o: A general framework for graph optimization, in: IEEE International Conference on Robotics and Automation, 2011, pp. 3607-3613. http://dx.doi.org/10.1109/ICRA.2011.5979949.

[15]

J. Wu, H. Christensen, J. Rehg, Visual place categorization: Problem, dataset, and algorithm, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009, pp. 4763-4770. http://dx.doi.org/10.1109/IROS.2009.5354164.

[16]

N. Winters, J. Gaspar, G. Lacey, J. Santos-Victor, Omni-directional vision for robot navigation, in: IEEE Workshop on Omnidirectional Vision, 2000, pp. 21-28.

[17]

J. Gaspar, N. Winters, J. Santos-Victor, Vision-based navigation and environmental representations with an omnidirectional camera, IEEE Trans. Robot. Autom., 16 (2000) 890-898.

[18]

I. Ulrich, I. Nourbakhsh, Appearance-based place recognition for topological localization, in: IEEE International Conference on Robotics and Automation, Vol. 2, 2000, pp. 1023-1029. http://dx.doi.org/10.1109/ROBOT.2000.844734.

[19]

J. Kosecka, L. Zhou, P. Barber, Z. Duric, Qualitative image based localization in indoors environments, in: International Conference on Computer Vision and Pattern Recognition, Vol.¿2, 2003, pp. II-3-II-8. http://dx.doi.org/10.1109/CVPR.2003.1211445.

[20]

D. Bradley, R. Patel, N. Vandapel, S. Thayer, Real-time image-based topological localization in large outdoor environments, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005, pp. 3670-3677. http://dx.doi.org/10.1109/IROS.2005.1545442.

[21]

C. Weiss, A. Masselli, Fast outdoor robot localization using integral invariants, in: International Conference on Computer Vision, 2007, pp. 1-10.

[22]

J. Wang, H. Zha, R. Cipolla, Efficient topological localization using orientation adjacency coherence histograms, in: International Conference on Pattern Recognition, 2006, pp. 271-274. http://dx.doi.org/10.1109/ICPR.2006.484.

[23]

A. Pronobis, B. Caputo, P. Jensfelt, H. Christensen, A discriminative approach to robust visual place recognition, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006, pp. 3829-3836. http://dx.doi.org/10.1109/IROS.2006.281789.

[24]

A. Oliva, A. Torralba, Modeling the shape of the scene: A holistic representation of the spatial envelope, Internat. J. Comput. Vis., 42 (2001) 145-175.

[25]

A.C. Murillo, P. Campos, J. Kosecka, J. Guerrero, Gist vocabularies in omnidirectional images for appearance based mapping and localization, in: IEEE Workshop on Omnidirectional Vision, Camera Networks and Non-classical Cameras, 2010.

[26]

N. Sunderhauf, P. Protzel, BRIEF-gist - closing the loop by simple means, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011, pp. 1234-1241. http://dx.doi.org/10.1109/IROS.2011.6094921.

[27]

A. Chapoulie, P. Rives, D. Filliat, Appearance-based segmentation of indoors and outdoors sequences of spherical views, in: IEEE International Conference on Robotics and Automation, 2013, pp. 1946-1951.

[28]

P. Lamon, I. Nourbakhsh, B. Jensen, R. Siegwart, Deriving and matching image fingerprint sequences for mobile robot localization, in: IEEE International Conference on Robotics and Automation, Vol.¿2, 2001, pp. 1609-1614. http://dx.doi.org/10.1109/ROBOT.2001.932841.

[29]

M. Liu, D. Scaramuzza, C. Pradalier, R. Siegwart, Q. Chen, Scene recognition with omnidirectional vision for topological map using lightweight adaptive descriptors, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009, pp. 116-121. http://dx.doi.org/10.1109/IROS.2009.5354131.

Digital Library

[30]

M. Liu, R. Siegwart, DP-FACT: Towards topological mapping and scene recognition with color for omnidirectional camera, in: IEEE International Conference on Robotics and Automation, 2012, pp. 3503-3508. http://dx.doi.org/10.1109/ICRA.2012.6225040.

[31]

E. Menegatti, T. Maeda, H. Ishiguro, Image-based memory for robot navigation using properties of omnidirectional images, Robot. Auton. Syst., 47 (2004) 251-267.

[32]

E. Menegatti, M. Zoccarato, E. Pagello, H. Ishiguro, Image-based monte carlo localisation with omnidirectional images, Robot. Auton. Syst., 48 (2004) 17-30.

[33]

D. Prasser, G. Wyeth, Probabilistic visual recognition of artificial landmarks for simultaneous localization and mapping, in: IEEE International Conference on Robotics and Automation, Vol.¿1, 2003, pp. 1291-1296. http://dx.doi.org/10.1109/ROBOT.2003.1241770.

[34]

M. Milford, G. Wyeth, Mapping a suburb with a single camera using a biologically inspired SLAM system, IEEE Trans. Robot., 24 (2008) 1038-1053.

[35]

M. Milford, G. Wyeth, SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights, in: IEEE International Conference on Robotics and Automation, 2012, pp. 1643-1649. http://dx.doi.org/10.1109/ICRA.2012.6224623.

[36]

W.L.D. Lui, R. Jarvis, A pure vision-based approach to topological SLAM, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010, pp. 3784-3791. http://dx.doi.org/10.1109/IROS.2010.5651115.

[37]

W.L.D. Lui, R. Jarvis, A pure vision-based topological SLAM system, Internat. J. Robot. Res., 31 (2012) 403-428.

Digital Library

[38]

H. Badino, D. Huber, T. Kanade, Real-time topometric localization, in: IEEE International Conference on Robotics and Automation, 2012, pp. 1635-1642. http://dx.doi.org/10.1109/ICRA.2012.6224716.

[39]

H. Lategahn, J. Beck, B. Kitt, C. Stiller, How to learn an illumination robust image feature for place recognition, in: IEEE Intelligent Vehicles Symposium, 2013, pp. 285-291.

[40]

N. Nourani-Vatani, P. Borges, J. Roberts, M. Srinivasan, On the use of optical flow for scene change detection and description, J. Intell. Robot. Syst. (2013).

[41]

J. Wu, J.M. Rehg, CENTRIST: A visual descriptor for scene categorization, IEEE Trans. Pattern Anal. Mach. Intell., 33 (2011) 1489-1501. http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.224

Digital Library

[42]

A. Bosch, A. Zisserman, X. Munoz, Representing shape with a spatial pyramid kernel, Image Process., 5 (2007) 401-408.

[43]

E. Fazl-Ersi, J.K. Tsotsos, Histogram of oriented uniform patterns for robust place recognition and categorization, Internat. J. Robot. Res., 31 (2012) 468-483.

Digital Library

[44]

L. Zhou, Z. Zhou, D. Hu, Scene classification using a multi-resolution Bag-of-features model, Pattern Recognition, 46 (2013) 424-433.

Digital Library

[45]

N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in: International Conference on Computer Vision and Pattern Recognition, 2005, pp. 886-893.

[46]

F. Werner, F. Maire, J. Sitte, Topological SLAM using fast vision techniques, in: Advances in Robotics, 2009, pp. 187-196.

[47]

C. Siagian, L. Itti, Rapid biologically-inspired scene classification using features shared with visual attention, IEEE Trans. Pattern Anal. Mach. Intell., 29 (2007) 300-312.

Digital Library

[48]

G. Singh, J. Kosecka, Visual loop closing using gist descriptors in Manhattan world, in: IEEE Workshop on Omnidirectional Vision, Camera Networks and Non-classical Camera, 2010.

[49]

A. Rituerto, A.C. Murillo, J. Guerrero, Semantic labeling for indoor topological mapping using a wearable catadioptric system, Robot. Auton. Syst., 62 (2013) 685-695.

[50]

Y. Liu, H. Zhang, Visual loop closure detection with a compact image descriptor, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012, pp. 1051-1056.

[51]

A. Chapoulie, P. Rives, D. Filliat, Topological segmentation of indoors/outdoors sequences of spherical views, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012, pp. 4288-4295. http://dx.doi.org/10.1109/IROS.2012.6385962.

[52]

M. Calonder, V. Lepetit, C. Strecha, P. Fua, BRIEF: Binary robust independent elementary features, in: Lecture Notes in Computer Science, vol. 6314, 2010, pp. 778-792.

[53]

R. Arroyo, P.F. Alcantarilla, L.M. Bergasa, J. Yebes, S. Gamez, Bidirectional loop closure detection on panoramas for visual navigation, in: IEEE Intelligent Vehicles Symposium, 2014, pp. 1378-1383. http://dx.doi.org/10.1109/IVS.2014.6856457.

[54]

X. Yang, K.-T. Cheng, Local difference binary for ultrafast and distinctive feature description, IEEE Trans. Pattern Anal. Mach. Intell., 36 (2014) 188-194.

[55]

R. Arroyo, P.F. Alcantarilla, L.M. Bergasa, J.J. Yebes, S. Bronte, Fast and effective visual place recognition using binary codes and disparity information, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014.

[56]

A. Tapus, N. Tomatis, R. Siegwart, Topological global localization and mapping with fingerprints and uncertainty, in: International Symposium on Experimental Robotics, 2004, pp. 18-21.

[57]

A. Tapus, R. Siegwart, Incremental robot mapping with fingerprints of places, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005, pp. 2429-2434. http://dx.doi.org/10.1109/IROS.2005.1544977.

[58]

L. Payá, L. Fernández, A. Gil, O. Reinoso, Map building and Monte Carlo localization using global appearance of omnidirectional images, Sensors, 10 (2010) 11468-11497.

[59]

A. Ranganathan, E. Menegatti, F. Dellaert, Bayesian inference in the space of topological maps, IEEE Trans. Robot., 22 (2006) 92-107.

Digital Library

[60]

M. Milford, G. Wyeth, D. Prasser, RatSLAM: A hippocampal model for simultaneous localization and mapping, in: IEEE International Conference on Robotics and Automation, 2004, pp. 403-408. http://dx.doi.org/10.1109/ROBOT.2004.1307183.

[61]

D. Prasser, M. Milford, G. Wyeth, Outdoor simultaneous localisation and mapping using RatSLAM, in: International Conference on Field and Service Robots, 2005, pp. 143-154.

[62]

A. Glover, W. Maddern, M. Milford, G. Wyeth, FAB-MAP + RatSLAM: Appearance-based SLAM for multiple times of day, in: IEEE International Conference on Robotics and Automation, 2010, pp. 3507-3512. http://dx.doi.org/10.1109/ROBOT.2010.5509547.

[63]

M. Agrawal, K. Konolige, M.R. Blas, CenSurE: Center surround extremas for realtime feature detection and matching, in: European Conference on Computer Vision, Vol. 5305, 2008, pp. 102-115.

[64]

D. Xu, H. Badino, D. Huber, Topometric localization on a road network, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014.

[65]

M. Milford, Visual route recognition with a handful of bits, in: Robotics: Systems and Science, 2013.

[66]

M. Milford, Vision-based place recognition: How low can you go?, Internat. J. Robot. Res., 32 (2013) 766-789.

Digital Library

[67]

E. Pepperell, P. Corke, M. Milford, All-environment visual place recognition with SMART, in: IEEE International Conference on Robotics and Automation, 2014, pp. 1612-1618.

[68]

J. Wu, H. Zhang, Y. Guan, An efficient visual loop closure detection method in a map of 20 million key locations, in: IEEE International Conference on Robotics and Automation, 2014, pp. 861-866. http://dx.doi.org/10.1109/ICRA.2014.6906955.

[69]

T. Tuytelaars, K. Mikolajczyk, Local invariant feature detectors: A survey, Foundations and Trends¿~Comput. Graphics Vision, 3 (2007) 177-280.

Digital Library

[70]

A. Schmidt, M. Kraft, A. Kasinski, An evaluation of image feature detectors and descriptors for robot navigation, in: Lecture Notes in Computer Science, vol. 6375, 2010, pp. 251-259.

[71]

O. Miksik, K. Mikolajczyk, Evaluation of local detectors and descriptors for fast feature matching, in: International Conference on Pattern Recognition, 2012, pp. 2681-2684.

[72]

K. Mikolajczyk, C. Schmid, A performance evaluation of local descriptors, IEEE Trans. Pattern Anal. Mach. Intell., 27 (2005) 1615-1630.

Digital Library

[73]

C. Harris, M. Stephens, A combined corner and edge detector, in: Alvey Vision Conference, 1988, pp. 147-151.

[74]

J. Shi, C. Tomasi, Good features to track, in: International Conference on Computer Vision and Pattern Recognition, 1994, pp. 593-600.

[75]

S. Smith, M. Brady, SUSAN - A new approach to low level image processing, Internat. J. Comput. Vis., 23 (1997) 45-78.

Digital Library

[76]

E. Rosten, T. Drummond, Machine learning for high-speed corner detection, in: Lecture Notes in Computer Science, vol.~1, 2006, pp. 430-443.

[77]

E. Rosten, R. Porter, T. Drummond, Faster and better: A machine learning approach to corner detection, IEEE Trans. Pattern Anal. Mach. Intell., 32 (2010) 105-119.

Digital Library

[78]

E. Rublee, V. Rabaud, K. Konolige, G. Bradski, ORB: An efficient alternative to SIFT or SURF, in: International Conference on Computer Vision, Vol. 95, 2011, pp. 2564-2571. http://dx.doi.org/10.1109/ICCV.2011.6126544.

[79]

E. Mair, G.D. Hager, D. Burschka, M. Suppa, G. Hirzinger, Adaptive and generic corner detection based on the accelerated segment test, in: Lecture Notes in Computer Science, vol. 6312, 2010, pp. 183-196.

[80]

S. Leutenegger, M. Chli, R. Siegwart, BRISK: Binary robust invariant scalable keypoints, in: International Conference on Computer Vision, 2011, pp. 2548-2555. http://dx.doi.org/10.1109/ICCV.2011.6126542.

[81]

D.G. Lowe, Distinctive image features from scale-invariant keypoints, Internat. J. Comput. Vis., 60 (2004) 91-110.

[82]

H. Bay, T. Tuytelaars, L. Van~Gool, SURF: Speeded up robust features, in: Lecture Notes in Computer Science, vol. 3951, 2006, pp. 404-417.

Digital Library

[83]

K. Konolige, J. Bowman, J. Chen, P. Mihelich, M. Calonder, V. Lepetit, P. Fua, View-based maps, Internat. J. Robot. Res., 29 (2010) 941-957.

Digital Library

[84]

M. Ebrahimi, W. Mayol-Cuevas, SUSurE: Speeded up surround extrema feature detector and descriptor for realtime applications, in: International Conference on Computer Vision and Pattern Recognition, 2009, pp. 9-14.

[85]

P.F. Alcantarilla, A. Bartoli, A.J. Davison, KAZE features, in: European Conference on Computer Vision, 2012, pp. 214-227.

[86]

P.F. Alcantarilla, J. Nuevo, A. Bartoli, Fast explicit diffusion for accelerated features in nonlinear scale spaces, in: British Machine Vision Conference, 2013.

[87]

J.-M. Morel, G. Yu, ASIFT: A new framework for fully affine invariant image comparison, SIAM J. Imaging Sci., 2 (2009) 438-469.

Digital Library

[88]

J. Matas, O. Chum, M. Urban, T. Pajdla, Robust wide baseline stereo from maximally stable extremal regions, in: British Machine Vision Conference, 2002, pp. 1-10.

[89]

Y. Ke, R. Sukthankar, PCA-SIFT: A more distinctive representation for local image descriptors, in: International Conference on Computer Vision and Pattern Recognition, 2004, pp. 506-513.

[90]

H. Andreasson, T. Duckett, Topological localization for mobile robots using omnidirectional vision and local features, in: IFAC Intelligent Autonomous Vehicles Symposium, 2008.

[91]

E. Tola, V. Lepetit, P. Fua, DAISY: An efficient dense descriptor applied to wide baseline stereo, IEEE Trans. Pattern Anal. Mach. Intell., 32 (2010) 815-830.

Digital Library

[92]

M.S. Sarfraz, O. Hellwich, Head pose estimation in face recognition across pose scenarios, in: International Conference on Computer Vision Theory and Applications, 2008, pp. 235-242.

[93]

A. Alahi, R. Ortiz, P. Vandergheynst, FREAK: Fast retina keypoint, in: International Conference on Computer Vision and Pattern Recognition, 2012, pp. 510-517.

[94]

T. Trzcinski, V. Lepetit, Efficient discriminative projections for compact binary descriptors, in: Lecture Notes in Computer Science, vol. 7572, 2012, pp. 228-242.

[95]

C. Strecha, A.M. Bronstein, M.M. Bronstein, P. Fua, LDAHash: Improved matching with smaller descriptors, IEEE Trans. Pattern Anal. Mach. Intell., 34 (2012) 66-78.

Digital Library

[96]

T. Trzcinski, C. Christoudias, P. Fua, V. Lepetit, Boosting binary keypoint descriptors, in: International Conference on Computer Vision and Pattern Recognition, 2013, pp. 2874-2881. http://dx.doi.org/10.1109/CVPR.2013.370.

Digital Library

[97]

J. Kosecka, X. Yang, Location recognition and global localization based on scale-invariant keypoints, in: European Conference on Computer Vision, 2004.

[98]

J. Kosecka, F. Li, Vision based topological Markov localization, in: IEEE International Conference on Robotics and Automation, Vol. 2, 2004, pp. 1481-1486. http://dx.doi.org/10.1109/ROBOT.2004.1308033.

[99]

F. Li, J. Kosecka, Probabilistic location recognition using reduced feature set, in: IEEE International Conference on Robotics and Automation, 2006, pp. 3405-3410. http://dx.doi.org/10.1109/ROBOT.2006.1642222.

[100]

H. Zhang, BoRF: Loop-closure detection with scale invariant visual features, in: IEEE International Conference on Robotics and Automation, 2011, pp. 3125-3130. http://dx.doi.org/10.1109/ICRA.2011.5980273.

[101]

H. Zhang, Indexing visual features: real-time loop closure detection using a tree structure, in: IEEE International Conference on Robotics and Automation, 2012, pp. 3613-3618. http://dx.doi.org/10.1109/ICRA.2012.6224741.

[102]

P. Rybski, F. Zacharias, J.-F. Lett, O. Masoud, M. Gini, N. Papanikolopoulos, Using Visual Features to Build Topological Maps of Indoor Environments, in: IEEE International Conference on Robotics and Automation, Vol. 1, 2003, pp. 850-855. http://dx.doi.org/10.1109/ROBOT.2003.1241699.

[103]

X. He, R. Zemel, V. Mnih, Topological map learning from outdoor image sequences, J. Field Robot., 23 (2006) 1091-1104.

[104]

D.G. Sabatta, Vision-based topological map building and localisation using persistent features, in: Robotics and Mechatronics Symposium, 2008, pp. 1-6.

[105]

E. Johns, G.-Z. Yang, Global localization in a dense continuous topological map, in: IEEE International Conference on Robotics and Automation, 2011, pp. 1032-1037.

[106]

A. Kawewong, S. Tangruamsub, O. Hasegawa, Position-invariant robust features for long-term recognition of dynamic outdoor scenes, IEICE Trans. Inf. Syst., E93-D (2010) 2587-2601.

[107]

A. Kawewong, N. Tongprasit, S. Tangruamsub, O. Hasegawa, Online and incremental appearance-based SLAM in highly dynamic environments, Internat. J. Robot. Res., 30 (2011) 33-55.

Digital Library

[108]

N. Tongprasit, A. Kawewong, O. Hasegawa, PIRF-Nav 2: Speeded-up online and incremental appearance-based slam in an indoor environment, in: IEEE Workshop on Applications of Computer Vision, 2011, pp. 145-152. http://dx.doi.org/10.1109/WACV.2011.5711496.

[109]

H. Morioka, S. Yi, O. Hasegawa, Vision-based mobile robot's SLAM and navigation in crowded environments, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011, pp. 3998-4005. http://dx.doi.org/10.1109/IROS.2011.6094847.

[110]

C. Valgren, A. Lilienthal, T. Duckett, Incremental topological mapping using omnidirectional vision, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006, pp. 3441-3447. http://dx.doi.org/10.1109/IROS.2006.282583.

[111]

C. Valgren, T. Duckett, A. Lilienthal, Incremental spectral clustering and its application to topological mapping, in: IEEE International Conference on Robotics and Automation, 2007, pp. 10-14.

[112]

C. Valgren, A. Lilienthal, SIFT, SURF and seasons: Long-term outdoor localization using local features, in: European Conference on Mobile Robotics, Vol. 128, 2007, pp. 1-6.

[113]

A. Ascani, E. Frontoni, A. Mancini, P. Zingaretti, Feature group matching for appearance-based localization, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008, pp. 3933-3938. http://dx.doi.org/10.1109/IROS.2008.4651023.

[114]

R. Anati, K. Daniilidis, Constructing topological maps using markov random fields and loop-closure detection, in: Advances in Neural Information Processing Systems, 2009, pp. 37-45.

[115]

Z. Zivkovic, B. Bakker, B. Krose, Hierarchical map building using visual landmarks and geometric constraints, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE/RSJ, 2005, pp. 2480-2485. http://dx.doi.org/10.1109/IROS.2005.1544951.

[116]

O. Booij, B. Terwijn, Z. Zivkovic, B. Krose, Navigation using an appearance based topological map, in: IEEE International Conference on Robotics and Automation, 2007, pp. 3927-3932. http://dx.doi.org/10.1109/ROBOT.2007.364081.

[117]

O. Booij, Z. Zivkovic, B. Krose, Efficient data association for view based SLAM using connected dominating sets, Robot. Auton. Syst., 57 (2009) 1225-1234.

Digital Library

[118]

F. Dayoub, G. Cielniak, T. Duckett, A sparse hybrid map for vision-guided mobile robots, in: European Conference on Mobile Robotics, 2011, pp. 213-218.

[119]

J.L. Blanco, J.A. Fernandez-Madrigal, J. Gonzalez, Towards a unified bayesian approach to hybrid metric-topological SLAM, IEEE Trans. Robot., 24 (2008) 259-270.

Digital Library

[120]

J.L. Blanco, J. Gonzalez, J.A. Fernandez-Madrigal, Subjective local maps for hybrid metric-topological SLAM, Robot. Auton. Syst., 57 (2009) 64-74.

Digital Library

[121]

S. Tully, H. Moon, D. Morales, G. Kantor, H. Choset, Hybrid localization using the hierarchical atlas, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007, pp. 2857-2864. http://dx.doi.org/10.1109/IROS.2007.4399553.

[122]

S. Tully, G. Kantor, H. Choset, F. Werner, A multi-hypothesis topological SLAM approach for loop closing on edge-ordered graphs, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009, pp. 4943-4948. http://dx.doi.org/10.1109/IROS.2009.5354255.

[123]

S. Segvic, A. Remazeilles, A. Diosi, F. Chaumette, A mapping and localization framework for scalable appearance-based navigation, Comput. Vision Image Understand., 113 (2009) 172-187.

Digital Library

[124]

A. Ramisa, A. Tapus, D. Aldavert, R. Toledo, R. Lopez de Mantaras, Robust vision-based robot localization using combinations of local feature region detectors, Auton. Robots, 27 (2009) 373-385.

Digital Library

[125]

H. Badino, D. Huber, T. Kanade, Visual topometric localization, in: IEEE Intelligent Vehicles Symposium, 2011, pp. 794-799. http://dx.doi.org/10.1109/IVS.2011.5940504.

[126]

F. Dayoub, T. Duckett, An adaptive appearance-based map for long-term topological localization of mobile robots, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008, pp. 3364-3369.

[127]

B. Bacca, J. Salvi, J. Batlle, X. Cufi, Appearance-based mapping and localisation using feature stability histograms, Electron. Lett., 46 (2010) 1120.

[128]

B. Bacca, J. Salvi, X. Cufi, Appearance-based mapping and localization for mobile robots using a feature stability histogram, Robot. Auton. Syst., 59 (2011) 840-857.

[129]

B. Bacca, J. Salvi, X. Cufi, Long-term mapping and localization using feature stability histograms, Robot. Auton. Syst., 61 (2013) 1539-1558.

Digital Library

[130]

A. Romero, M. Cazorla, Topological SLAM using omnidirectional images: merging feature detectors and graph-matching, in: Lecture Notes in Computer Science, vol. 6474, 2010, pp. 464-475.

[131]

A. Romero, M. Cazorla, Topological visual mapping in robotics, Cognitive Process., 13 (2012) 305-308.

[132]

A. Majdik, Y. Albers-Schoenberg, D. Scaramuzza, MAV urban localization from google street view data, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013.

[133]

M. Saedan, C.W. Lim, M. Ang, Appearance-based slam with map loop closing using an omnidirectional camera, in: International Conference on Advanced Intelligent Mechatronics, 2007, pp. 1-6. http://dx.doi.org/10.1109/AIM.2007.4412510.

[134]

J. Kessler, A. König, H.-M. Gross, An improved sensor model on appearance based SLAM, in: Autonome Mobile Systeme, vol. 216487, 2009, pp. 153-160.

[135]

L. Maohai, W. Han, S. Lining, C. Zesu, Robust omnidirectional mobile robot topological navigation system using omnidirectional vision, Engrg. Appl. Artificial Intell., 26 (2013) 1942-1952.

Digital Library

[136]

E. Garcia-Fidalgo, A. Ortiz, Probabilistic appearance-based mapping and localization using visual features, in: Iberian Conference on Pattern Recognition and Image Analysis, Funchal (Portugal), 2013, pp. 277-285.

[137]

E. Garcia-Fidalgo, A. Ortiz, Vision-based topological mapping and localization by means of local invariant features and map refinement, Robotica. http://dx.doi.org/10.1017/S0263574714000782. Available online: 8 April 2014.

[138]

H. Zhang, B. Li, D. Yang, Keyframe detection for appearance-based visual SLAM, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010, pp. 2071-2076. http://dx.doi.org/10.1109/IROS.2010.5650625.

[139]

B. Lisien, D. Morales, D. Silver, G. Kantor, I.M. Rekleitis, H. Choset, The hierarchical atlas, IEEE Trans. Robot., 21 (2005) 473-481.

Digital Library

[140]

S. Tully, G. Kantor, H. Choset, A unified bayesian framework for global localization and SLAM in hybrid metric/topological maps, Internat. J. Robot. Res., 31 (2012) 271-288.

Digital Library

[141]

R.C. Atkinson, R.M. Shiffrin, Human memory: A proposed system and its control processes, Psychol. Learn. Motivation Adv. Res. Theory, 2 (1968) 89-105.

[142]

J. Sivic, A. Zisserman, Video google: A text retrieval approach to object matching in videos, in: International Conference on Computer Vision, 2003, pp. 1470-1477. http://dx.doi.org/10.1109/ICCV.2003.1238663.

[143]

J. Wang, R. Cipolla, H. Zha, Vision-based global localization using a visual vocabulary, in: IEEE International Conference on Robotics and Automation, 2005, pp. 4230-4235.

[144]

J. Wang, H. Zha, R. Cipolla, Coarse-to-fine vision-based localization by indexing scale-invariant features, IEEE Trans. Syst. Man Cybern., 36 (2006) 413-422.

Digital Library

[145]

F. Fraundorfer, C. Engels, D. Nister, Topological mapping, localization and navigation using image collections, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007, pp. 3872-3877. http://dx.doi.org/10.1109/IROS.2007.4399123.

[146]

M. Cummins, P. Newman, Probabilistic appearance based navigation and loop closing, in: IEEE International Conference on Robotics and Automation, 2007, pp. 2042-2048.

[147]

M. Cummins, P. Newman, FAB-MAP: Probabilistic localization and mapping in the space of appearance, Internat. J. Robot. Res., 27 (2008) 647-665.

Digital Library

[148]

M. Cummins, P. Newman, Accelerated appearance-only SLAM, in: IEEE International Conference on Robotics and Automation, 2008, pp. 1828-1833. http://dx.doi.org/10.1109/ROBOT.2008.4543473.

[149]

M. Cummins, P. Newman, Accelerating FAB-MAP with concentration inequalities, IEEE Trans. Robot., 26 (2010) 1042-1050.

Digital Library

[150]

M. Cummins, P. Newman, Highly scalable appearance-only SLAM - FAB-MAP 2.0., in: Robotics: Systems and Science, 2009.

[151]

M. Cummins, P. Newman, Appearance-only SLAM at large scale with FAB-MAP 2.0, Internat. J. Robot. Res., 30 (2011) 1100-1123.

Digital Library

[152]

P. Newman, G. Sibley, M. Smith, M. Cummins, A. Harrison, C. Mei, I. Posner, R. Shade, D. Schroeter, L. Murphy, W. Churchill, D. Cole, I. Reid, Navigating, recognizing and describing urban spaces with vision and lasers, Internat. J. Robot. Res., 28 (2009) 1406-1433.

Digital Library

[153]

W. Maddern, M. Milford, G. Wyeth, Continuous appearance-based trajectory SLAM, in: IEEE International Conference on Robotics and Automation, 2011, pp. 3595-3600.

[154]

W. Maddern, M. Milford, G. Wyeth, CAT-SLAM: Probabilistic localisation and mapping using a continuous appearance-based trajectory, Internat. J. Robot. Res., 31 (2012) 429-451.

Digital Library

[155]

W. Maddern, M. Milford, G. Wyeth, Towards persistent indoor appearance-based localization, mapping and navigation using CAT-graph, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012, pp. 4224-4230.

[156]

R. Paul, P. Newman, FAB-MAP 3D: Topological mapping with spatial and visual appearance, in: IEEE International Conference on Robotics and Automation, 2010, pp. 2649-2656. http://dx.doi.org/10.1109/ROBOT.2010.5509587.

[157]

E. Johns, G.-Z. Yang, Feature co-occurrence maps: Appearance-based localisation throughout the day, in: IEEE International Conference on Robotics and Automation, 2013, pp. 3212-3218.

[158]

E. Johns, G.-Z. Yang, Dynamic scene models for incremental, long term, appearance based localisation, in: IEEE International Conference on Robotics and Automation, 2013, pp. 2731-2736.

[159]

D. Galvez-Lopez, J. Tardos, Real-time loop detection with bags of binary words, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011, pp. 51-58. http://dx.doi.org/10.1109/IROS.2011.6094885.

[160]

D. Galvez-Lopez, J. Tardos, Bags of binary words for fast place recognition in image sequences, IEEE Trans. Robot., 28 (2012) 1188-1197.

Digital Library

[161]

R. Mur-Artal, J.D. Tardos, Fast relocalisation and loop closing in keyframe-based SLAM, in: IEEE International Conference on Robotics and Automation, 2014, pp. 846-853. http://dx.doi.org/10.1109/ICRA.2014.6906953.

[162]

A. Ranganathan, F. Dellaert, Online probabilistic topological mapping, Internat. J. Robot. Res., 30 (2011) 755-771.

Digital Library

[163]

C. Cadena, D. Galvez-Lopez, F. Ramos, J. Tardos, J. Neira, Robust place recognition with stereo cameras, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010, pp. 5182-5189. http://dx.doi.org/10.1109/IROS.2010.5650234.

[164]

T. Ciarfuglia, G. Costante, P. Valigi, E. Ricci, A discriminative approach for appearance based loop closing, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012, pp. 3837-3843.

[165]

A. Majdik, D. Galvez-Lopez, G. Lazea, J. Castellanos, Adaptive appearance based loop-closing in heterogeneous environments, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011, pp. 1256-1263. http://dx.doi.org/10.1109/IROS.2011.6094537.

[166]

G. Schindler, M. Brown, R. Szeliski, City-scale location recognition, in: International Conference on Computer Vision and Pattern Recognition, 2007, pp. 1-7. http://dx.doi.org/10.1109/CVPR.2007.383150.

[167]

S. Achar, C. Jawahar, K. Madhava Krishna, Large scale visual localization in urban environments, in: IEEE International Conference on Robotics and Automation, 2011, pp. 5642-5648. http://dx.doi.org/10.1109/ICRA.2011.5979925.

[168]

J.H. Lee, G. Zhang, J. Lim, I.H. Suh, Place recognition using straight lines for vision-based SLAM, in: IEEE International Conference on Robotics and Automation, 2013, pp. 3799-3806. http://dx.doi.org/10.1109/ICRA.2013.6631111.

[169]

D. Filliat, A visual bag of words method for interactive qualitative localization and mapping, in: IEEE International Conference on Robotics and Automation, 2007, pp. 3921-3926. http://dx.doi.org/10.1109/ROBOT.2007.364080.

[170]

A. Angeli, S. Doncieux, J.-A. Meyer, D. Filliat, Real-time visual loop-closure detection, in: IEEE International Conference on Robotics and Automation, 2008, pp. 1842-1847. http://dx.doi.org/10.1109/ROBOT.2008.4543475.

[171]

A. Angeli, D. Filliat, S. Doncieux, J.-A. Meyer, A fast and incremental method for loop-closure detection using bags of visual words, IEEE Trans. Robot., 24 (2008) 1027-1037.

[172]

A. Angeli, S. Doncieux, J.-A. Meyer, D. Filliat, Incremental vision-based topological SLAM, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008, pp. 1031-1036. http://dx.doi.org/10.1109/IROS.2008.4650675.

[173]

M. Labbe, F. Michaud, Memory management for real-time appearance-based loop closure detection, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011, pp. 1271-1276. http://dx.doi.org/10.1109/IROS.2011.6094602.

[174]

M. Labbe, F. Michaud, Appearance-based loop closure detection for online large-scale and long-term operation, IEEE Trans. Robot., 29 (2013) 734-745.

Digital Library

[175]

T. Nicosevici, R. Garcia, On-line visual vocabularies for robot navigation and mapping, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009, pp. 205-212. http://dx.doi.org/10.1109/IROS.2009.5354392.

Digital Library

[176]

T. Nicosevici, R. Garcia, Automatic visual bag-of-words for online robot navigation and mapping, IEEE Trans. Robot., 28 (2012) 886-898.

Digital Library

[177]

L. Murphy, G. Sibley, Incremental unsupervised topological place discovery, in: IEEE International Conference on Robotics and Automation, 2014, pp. 1312-1318.

[178]

E. Garcia-Fidalgo, A. Ortiz, On the use of binary feature descriptors for loop closure detection, in: IEEE International Conference on Emerging Technologies and Factory Automation, 2014, pp. 1-8.

[179]

G. Csurka, C. Dance, L. Fan, J. Willamowski, C. Bray, Visual categorization with bags of keypoints, in: European Conference on Computer Vision, Vol. 1, 2004, pp. 1-22. http://dx.doi.org/10.1234/12345678.

[180]

F.-F. Li, P. Perona, A Bayesian hierarchical model for learning natural scene categories, in: International Conference on Computer Vision and Pattern Recognition, 2005, pp. 524-531.

[181]

D. Nister, H. Stewenius, Scalable recognition with a vocabulary tree, in: International Conference on Computer Vision and Pattern Recognition, Vol.¿2, 2006, pp. 2161-2168. http://dx.doi.org/10.1109/CVPR.2006.264.

[182]

A. Glover, W. Maddern, M. Warren, S. Reid, M. Milford, G. Wyeth, OpenFABMAP: An open source toolbox for appearance-based loop closure detection, in: IEEE International Conference on Robotics and Automation, 2012, pp. 4730-4735.

[183]

A. Ranganathan, F. Dellaert, A rao-blackwellized particle filter for topological mapping, in: IEEE International Conference on Robotics and Automation, 2006, pp. 810-817. http://dx.doi.org/10.1109/ROBOT.2006.1641809.

[184]

Y. Latif, C. Cadena, J. Neira, Realizing, reversing, recovering: incremental robust loop closing over time using the iRRR algorithm, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012, pp. 4211-4217.

[185]

E. Eade, T. Drummond, Unified loop closing and recovery for real time monocular SLAM, in: British Machine Vision Conference, 2008, pp. 1-10.

[186]

T. Botterill, S. Mills, R. Green, Bag-of-words-driven, single-camera simultaneous localization and mapping, J. Field Robot., 28 (2011) 204-226.

Digital Library

[187]

V. Pradeep, G. Medioni, J. Weiland, Visual loop closing using multi-resolution SIFT grids in metric-topological SLAM, in: International Conference on Computer Vision and Pattern Recognition, 2009, pp. 1438-1445.

[188]

T. Goedemé, M. Nuttin, T. Tuytelaars, L. Van Gool, Markerless computer vision based localization using automatically generated topological maps, in: European Navigation Conference, 2004, pp. 235-243.

[189]

T. Goedemé, M. Nuttin, T. Tuytelaars, L. Van Gool, Omnidirectional vision based topological navigation, Internat. J. Comput. Vis., 74 (2007) 219-236.

Digital Library

[190]

A.C. Murillo, C. Sagues, J. Guerrero, From omnidirectional images to hierarchical localization, Robot. Auton. Syst., 55 (2007) 372-382.

Digital Library

[191]

A.C. Murillo, J. Guerrero, C. Sagues, SURF features for efficient robot localization with omnidirectional images, in: IEEE International Conference on Robotics and Automation, 2007, pp. 3901-3907. http://dx.doi.org/10.1109/ROBOT.2007.364077.

[192]

J. Wang, Y. Yagi, Efficient topological localization using global and local feature matching, Internat. J. Adv. Robot. Syst. (2013).

[193]

C. Weiss, A. Masselli, A. Zell, Fast vision-based localization for outdoor robots using a combination of global image features, in: IFAC Intelligent Autonomous Vehicles Symposium, 2007, pp. 119-124.

[194]

C. Weiss, H. Tamimi, A. Masselli, A. Zell, A hybrid approach for vision-based outdoor robot localization using global and local image features, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007, pp. 1047-1052. http://dx.doi.org/10.1109/IROS.2007.4398959.

[195]

C. Siagian, L. Itti, Biologically inspired mobile robot vision localization, IEEE Trans. Robot., 25 (2009) 861-873.

Digital Library

[196]

A. Chapoulie, P. Rives, D. Filliat, A spherical representation for efficient visual loop closing, in: International Conference on Computer Vision, 2011, pp. 335-342. http://dx.doi.org/10.1109/ICCVW.2011.6130261.

[197]

M.-L. Wang, H.-Y. Lin, A hull census transform for scene change detection and recognition towards topological map building, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010, pp. 548-553. http://dx.doi.org/10.1109/IROS.2010.5652817.

[198]

H.-Y. Lin, Y.-H. Lin, J.-W. Yao, Scene change detection and topological map construction using omnidirectional image sequences, in: International Conference on Machine Vision and Applications, 2013, pp. 4-7.

[199]

J. Wang, Y. Yagi, Robust location recognition based on efficient feature integration, in: IEEE International Conference on Robotics and Biomimetics, 2012, pp. 97-101. http://dx.doi.org/10.1109/ROBIO.2012.6490950.

[200]

H. Korrapati, F. Uzer, Y. Mezouar, Hierarchical visual mapping with omnidirectional images, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013, pp. 3684-3690.

[201]

H. Korrapati, Y. Mezouar, Vision-based sparse topological mapping, Robot. Auton. Syst., 62 (2014) 1259-1270.

Digital Library

[202]

S. Lazebnik, C. Schmid, J. Ponce, Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories, in: International Conference on Computer Vision and Pattern Recognition, Vol. 2, 2006, pp. 2169-2178. http://dx.doi.org/10.1109/CVPR.2006.68.

[203]

J. Hou, W.-X. Liu, X. E, Q. Xia, N.-M. Qi, An experimental study on the universality of visual vocabularies, J. Vis. Commun. Image Represent., 24 (2013) 1204-1211.

Digital Library

Cited By

Regani SHu YWang BLiu K(2022)Wifi-based robust indoor localization for daily activity monitoringProceedings of the 1st ACM Workshop on Mobile and Wireless Sensing for Smart Healthcare10.1145/3556551.3561187(1-6)Online publication date: 21-Oct-2022
https://dl.acm.org/doi/10.1145/3556551.3561187
Kathuria TXu YChakhachiro TYang XGhaffari M(2022)Providers-Clients-Robots: Framework for spatial-semantic planning for shared understanding in human-robot interaction2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)10.1109/RO-MAN53752.2022.9900525(1099-1106)Online publication date: 29-Aug-2022
https://dl.acm.org/doi/10.1109/RO-MAN53752.2022.9900525
Tsintotas KAn SPapapetros IKonstantinidis FSirakoulis GGasteratos A(2022)Dimensionality reduction through visual data resampling for low-storage loop-closure detection2022 IEEE International Conference on Imaging Systems and Techniques (IST)10.1109/IST55454.2022.9827748(1-6)Online publication date: 21-Jun-2022
https://dl.acm.org/doi/10.1109/IST55454.2022.9827748
Show More Cited By

Vision-based topological mapping and localization methods
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks

Recommendations

Vision-based sparse topological mapping

Most of the existing appearance-based topological mapping algorithms produce dense topological maps in which each image stands as a node in the topological graph. Sparser maps can be built by representing groups of visually similar images of a sequence ...
Visual Topological Mapping Using an Appearance-Based Location Selection Method
Towards Autonomous Robotic Systems
Abstract
Visual representation of an environment in topological maps is a challenging task since different factors such as variable lighting conditions, viewpoints, mobility of robots, dynamic and featureless appearance, etc., can affect the ...
S-PTAM

This paper describes a real-time feature-based stereo SLAM system that is robust and accurate in a wide variety of conditionsindoors, outdoors, with dynamic objects, changing light conditions, fast robot motions and large-scale loops. Our system follows ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Robotics and Autonomous Systems

Robotics and Autonomous Systems Volume 64, Issue C

February 2015

143 pages

ISSN:0921-8890

Issue’s Table of Contents

Copyright © Elsevier B.V.

Publisher

North-Holland Publishing Co.

Netherlands

Publication History

Published: 01 February 2015

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

37
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 08 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Regani SHu YWang BLiu K(2022)Wifi-based robust indoor localization for daily activity monitoringProceedings of the 1st ACM Workshop on Mobile and Wireless Sensing for Smart Healthcare10.1145/3556551.3561187(1-6)Online publication date: 21-Oct-2022
https://dl.acm.org/doi/10.1145/3556551.3561187
Kathuria TXu YChakhachiro TYang XGhaffari M(2022)Providers-Clients-Robots: Framework for spatial-semantic planning for shared understanding in human-robot interaction2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)10.1109/RO-MAN53752.2022.9900525(1099-1106)Online publication date: 29-Aug-2022
https://dl.acm.org/doi/10.1109/RO-MAN53752.2022.9900525
Tsintotas KAn SPapapetros IKonstantinidis FSirakoulis GGasteratos A(2022)Dimensionality reduction through visual data resampling for low-storage loop-closure detection2022 IEEE International Conference on Imaging Systems and Techniques (IST)10.1109/IST55454.2022.9827748(1-6)Online publication date: 21-Jun-2022
https://dl.acm.org/doi/10.1109/IST55454.2022.9827748
Möller RFurnari ABattiato SHärmä AFarinella G(2022)A survey on human-aware robot navigationRobotics and Autonomous Systems10.1016/j.robot.2021.103837145:COnline publication date: 22-Apr-2022
https://dl.acm.org/doi/10.1016/j.robot.2021.103837
Cabrera JCebollada SFlores MReinoso ÓPayá L(2022)Training, Optimization and Validation of a CNN for Room Retrieval and Description of Omnidirectional ImagesSN Computer Science10.1007/s42979-022-01127-83:4Online publication date: 5-May-2022
https://dl.acm.org/doi/10.1007/s42979-022-01127-8
Humenberger MCabon YPion NWeinzaepfel PLee DGuérin NSattler TCsurka G(2022)Investigating the Role of Image Retrieval for Visual LocalizationInternational Journal of Computer Vision10.1007/s11263-022-01615-7130:7(1811-1836)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s11263-022-01615-7
Company-Corcoles JGarcia-Fidalgo EOrtiz A(2022)Appearance-based loop closure detection combining lines and learned points for low-textured environmentsAutonomous Robots10.1007/s10514-021-10032-746:3(451-467)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s10514-021-10032-7
Tsintotas KBampis LAn SFragulis GMouroutsos SGasteratos A(2021)Sequence-based mapping for probabilistic visual loop-closure detection2021 IEEE International Conference on Imaging Systems and Techniques (IST)10.1109/IST50367.2021.9651458(1-6)Online publication date: 24-Aug-2021
https://dl.acm.org/doi/10.1109/IST50367.2021.9651458
Cebollada SPayá LFlores MPeidró AReinoso O(2021)A state-of-the-art review on mobile robotics tasks using artificial intelligence and visual dataExpert Systems with Applications: An International Journal10.1016/j.eswa.2020.114195167:COnline publication date: 1-Apr-2021
https://dl.acm.org/doi/10.1016/j.eswa.2020.114195
Kassir MPalhang MAhmadzadeh M(2021)Two Efficient Visual Methods for Segment Self-localizationSN Computer Science10.1007/s42979-021-00492-02:2Online publication date: 7-Feb-2021
https://dl.acm.org/doi/10.1007/s42979-021-00492-0
Show More Cited By

View Options

View options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents