Abstract
SLAM with RGB-D cameras is a very active field in Computer Vision as well as Robotics. Dense methods using all depth and intensity information showed best results in the past. However, usually they were developed and evaluated with RGB-D cameras using Pattern Projection like the Kinect v1 or Xtion Pro. Recently, Time-of-Flight (ToF) cameras like the Kinect v2 or Google Tango were released promising higher quality. While the overall accuracy increases for these ToF cameras, noisy pixels are introduced close to discontinuities, in the image corners and on dark/glossy surfaces. These inaccuracies need to be specially addressed for dense SLAM. Thus, we present a new Dense Noise Aware SLAM (DNA-SLAM), which considers explicitly the noise characteristics of ToF RGB-D cameras with a sophisticated weighting scheme. In a rigorous evaluation on public benchmarks we show the superior accuracy of our algorithm compared to the state-of-the-art.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Davison, A.J.: Real-time simultaneous localisation and mapping with a single camera. In: International Conference on Computer Vision (ICCV), pp. 1403–1410. IEEE (2003)
Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: International Symposium on Mixed and Augmented Reality (ISMAR), pp. 225–234. IEEE (2007)
Newcombe, R.A., Lovegrove, S.J., Davison, A.J.: DTAM: Dense tracking and mapping in real-time. In: International Conference on Computer Vision (ICCV), pp. 2320–2327. IEEE (2011)
Engel, J., Schöps, T., Cremers, D.: LSD-SLAM: large-scale direct monocular SLAM. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 834–849. Springer, Cham (2014). doi:10.1007/978-3-319-10605-2_54
Microsoft: (Kinect v2). www.microsoft.com/en-us/kinectforwindows/
Google: (Tango). www.google.com/atap/project-tango/
Engelhard, N., Endres, F., Hess, J., Sturm, J., Burgard, W.: Real-time 3D visual slam with a hand-held RGB-D camera. In: RGB-D Workshop on 3D Perception in Robotics at the European Robotics Forum, vol. 180 (2011)
Huang, A.S., Bachrach, A., Henry, P., Krainin, M., Maturana, D., Fox, D., Roy, N.: Visual odometry and mapping for autonomous flight using an RGB-D camera. In: International Symposium on Robotics Research (ISRR), vol. 2 (2011)
Kerl, C., Sturm, J., Cremers, D.: Robust odometry estimation for RGB-D cameras. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3748–3754. IEEE (2013)
Gutierrez-Gomez, D., Mayol-Cuevas, W., Guerrero, J.: Dense RGB-D visual odometry using inverse depth. Robot. Auton. Syst. 75, 571–583 (2016)
Brunetto, N., Fioraio, N., Stefano, L.: Interactive RGB-D SLAM on mobile devices. In: Jawahar, C.V., Shan, S. (eds.) ACCV 2014. LNCS, vol. 9010, pp. 339–351. Springer, Cham (2015). doi:10.1007/978-3-319-16634-6_25
Belter, D., Nowicki, M., Skrzypczyński, P.: On the performance of pose-based RGB-D visual navigation systems. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9004, pp. 407–423. Springer, Cham (2015). doi:10.1007/978-3-319-16808-1_28
Ma, L., Kerl, C., Stueckler, J., Cremers, D.: CPA-SLAM: consistent plane-model alignment for direct RGB-D slam. In: International Conference on Robotics and Automation (ICRA) (2016)
Whelan, T., Johannsson, H., Kaess, M., Leonard, J.J., McDonald, J.: Robust real-time visual odometry for dense RGB-D mapping. In: International Conference on Robotics and Automation (ICRA), pp. 5724–5731. IEEE (2013)
Steinbruecker, F., Sturm, J., Cremers, D.: Real-time visual odometry from dense RGB-D images. In: International Conference on Computer Vision Workshop (ICCV Workshop) (2011)
Audras, C., Comport, A., Meilland, M., Rives, P.: Real-time dense appearance-based slam for RGB-D sensors. In: Australasian Conference on Robotics and Automation (ACRA) (2011)
Klose, S., Heise, P., Knoll, A.: Efficient compositional approaches for real-time robust direct visual odometry from RGB-D data. In: International Conference on Intelligent Robots and Systems (IROS), pp. 1100–1106. IEEE (2013)
Kerl, C., Sturm, J., Cremers, D.: Dense visual slam for RGB-D cameras. In: International Conference on Intelligent Robot Systems (IROS) (2013)
Meilland, M., Comport, A.I.: On unifying key-frame and voxel-based dense visual slam at large scales. In: International Conference on Intelligent Robots and Systems (IROS), pp. 3677–3683. IEEE (2013)
Kerl, C., Stueckler, J., Cremers, D.: (Dense continuous-time tracking and mapping with rolling shutter RGB-D cameras)
Wasenmüller, O., Stricker, D.: Comparison of kinect v1 and v2 depth images in terms of accuracy and precision. In: Chen, C.-S., Lu, J., Ma, K.-K. (eds.) ACCV 2016 Workshops, Part II. LNCS, vol. 10116, pp. 34–45. Springer, Cham (2017)
Ma, Y., Soatto, S., Kosecka, J., Sastry, S.S.: An invitation to 3-d vision: from images to geometric models, vol. 26. Springer Science & Business Media, New York (2012)
Besl, P.J., McKay, N.D.: Method for registration of 3-d shapes. In: Robotics-DL Tentative, pp. 586–606. International Society for Optics and Photonics (1992)
Tykkälä, T., Audras, C., Comport, A.I.: Direct iterative closest point for real-time visual odometry. In: International Conference on Computer Vision Workshops (ICCV Workshops), pp. 2050–2056. IEEE (2011)
Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of RGB-D slam systems. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 573–580. IEEE (2012)
Wasenmüller, O., Meyer, M., Stricker, D.: CoRBS: comprehensive RGB-D benchmark for slam using kinect v2. In: IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE (2016)
Rusinkiewicz, S., Levoy, M.: Efficient variants of the ICP algorithm. In: IEEE International Conference on 3D Digital Imaging and Modeling, pp. 145–152. IEEE (2001)
Newcombe, R.A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A.J., Kohi, P., Shotton, J., Hodges, S., Fitzgibbon, A.: Kinectfusion: real-time dense surface mapping and tracking. In: IEEE International Symposium on Mixed and Augmented Reality (ISMAR) (2011)
Lin, Y.C., Chen, C.Y., Huang, S.W., Huang, P.S., Chen, C.F.: Registration and merging of large scale range data using an improved ICP algorithm approach. In: International Conference Image and Vision Computing (IVCNZ) (2011)
Wasenmüller, O., Meyer, M., Stricker, D.: Augmented reality 3D discrepancy check in industrial applications. In: IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 125–134. IEEE (2016)
Lee, D., Kim, H., Myung, H.: Gpu-based real-time RGB-D 3D slam. In: International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp. 46–48. IEEE (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Wasenmüller, O., Ansari, M.D., Stricker, D. (2017). DNA-SLAM: Dense Noise Aware SLAM for ToF RGB-D Cameras. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10116. Springer, Cham. https://doi.org/10.1007/978-3-319-54407-6_42
Download citation
DOI: https://doi.org/10.1007/978-3-319-54407-6_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-54406-9
Online ISBN: 978-3-319-54407-6
eBook Packages: Computer ScienceComputer Science (R0)