research-article

MonoSLAM: Real-Time Single Camera SLAM

Authors:

Andrew J. Davison,

Nicholas D. Molton,

Olivier StasseAuthors Info & Claims

IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 29, Issue 6

Pages 1052 - 1067

https://doi.org/10.1109/TPAMI.2007.1049

Published: 01 June 2007 Publication History

Abstract

We present a real-time algorithm which can recover the 3D trajectory of a monocular camera, moving rapidly through a previously unknown scene. Our system, which we dub MonoSLAM, is the first successful application of the SLAM methodology from mobile robotics to the "pure vision” domain of a single uncontrolled camera, achieving real time but drift-free performance inaccessible to Structure from Motion approaches. The core of the approach is the online creation of a sparse but persistent map of natural landmarks within a probabilistic framework. Our key novel contributions include an active approach to mapping and measurement, the use of a general motion model for smooth camera movement, and solutions for monocular feature initialization and feature orientation estimation. Together, these add up to an extremely efficient and robust algorithm which runs at 30 Hz with standard PC and camera hardware. This work extends the range of robotic systems in which SLAM can be usefully applied, but also opens up new areas. We present applications of MonoSLAM to real-time 3D localization and mapping for a high-performance full-size humanoid robot and live augmented reality with a hand-held camera.

References

[1]

A.W. Fitzgibbon and A. Zisserman, “Automatic Camera Recovery for Closed or Open Image Sequences,” Proc. European Conf. Computer Vision, pp. 311-326, June 1998.

Digital Library

[2]

M. Pollefeys, R. Koch, and L.V. Gool, “Self-Calibration and Metric Reconstruction in Spite of Varying and Unknown Internal Camera Parameters,” Proc. Sixth Int'l Conf. Computer Vision, pp. 90-96, 1998.

Digital Library

[3]

“2d3 Web Based Literature,” URL http://www.2d3.com/, 2005.

[4]

A. Rahimi, L.P. Morency, and T. Darrell, “Reducing Drift in Parametric Motion Tracking,” Proc. Eighth Int'l Conf. Computer Vision, pp. 315-322, 2001.

[5]

A.J. Davison, “Real-Time Simultaneous Localisation and Mapping with a Single Camera,” Proc. Ninth Int'l Conf. Computer Vision, 2003.

Digital Library

[6]

A.J. Davison, Y.G. Cid, and N. Kita, “Real-Time 3D SLAM with Wide-Angle Vision,” Proc. IFAC Symp. Intelligent Autonomous Vehicles, July 2004.

[7]

N.D. Molton, A.J. Davison, and I.D. Reid, “Locally Planar Patch Features for Real-Time Structure from Motion,” Proc. 15th British Machine Vision Conf., 2004.

[8]

C.G. Harris and J.M. Pike, “3D Positional Integration from Image Sequences,” Proc. Third Alvey Vision Conf., pp. 233-236, 1987.

[9]

N. Ayache, Artificial Vision for Mobile Robots: Stereo Vision and Multisensory Perception. MIT Press, 1991.

Digital Library

[10]

P.A. Beardsley, I.D. Reid, A. Zisserman, and D.W. Murray, “Active Visual Navigation Using Non-Metric Structure,” Proc. Fifth Int'l Conf. Computer Vision, pp. 58-65, 1995.

Digital Library

[11]

R. Smith, M. Self, and P. Cheeseman, “A Stochastic Map for Uncertain Spatial Relationships,” Proc. Fourth Int'l Symp. Robotics Research, 1987.

Digital Library

[12]

P. Moutarlier and R. Chatila, “Stochastic Multisensory Data Fusion for Mobile Robot Location and Environement Modelling,” Proc. Int'l Symp. Robotics Research, 1989.

[13]

J.J. Leonard, “Directed Sonar Sensing for Mobile Robot Navigation,” PhD dissertation, Univ. of Oxford, 1990.

Digital Library

[14]

J. Manyika, “An Information-Theoretic Approach to Data Fusion and Sensor Management,” PhD dissertation, Univ. of Oxford, 1993.

[15]

S. Betgé-Brezetz, P. Hébert, R. Chatila, and M. Devy, “Uncertain Map Making in Natural Environments,” Proc. IEEE Int'l Conf. Robotics and Automation, 1996.

[16]

M. Csorba, “Simultaneous Localisation and Mapping,” PhD dissertation, Univ. of Oxford, 1997.

[17]

J.A. Castellanos, “Mobile Robot Localization and Map Building: A Multisensor Fusion Approach,” PhD dissertation, Universidad de Zaragoza, Spain, 1998.

[18]

A.J. Davison, “Mobile Robot Navigation Using Active Vision,” PhD dissertation, Univ. of Oxford, 1998.

[19]

P.M. Newman, “On the Structure and Solution of the Simultaneous Localization and Map Building Problem,” PhD dissertation, Univ. of Sydney, 1999.

[20]

M. Bosse, P. Newman, J. Leonard, M. Soika, W. Feiten, and S. Teller, “An Atlas Framework for Scalable Mapping,” Proc. IEEE Int'l Conf. Robotics and Automation, 2003.

[21]

P.M. Newman and J.J. Leonard, “Consistent Convergent Constant Time SLAM,” Proc. Int'l Joint Conf. Artificial Intelligence, 2003.

[22]

M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit, “FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem,” Proc. AAAI Nat'l Conf. Artificial Intelligence, 2002.

Digital Library

[23]

P.M. Newman, J.J. Leonard, J. Neira, and J. Tardós, “Explore and Return: Experimental Validation of Real Time Concurrent Mapping and Localization,” Proc. IEEE Int'l Conf. Robotics and Automation, pp. 1802-1809, 2002.

[24]

K. Konolige and J.-S. Gutmann, “Incremental Mapping of Large Cyclic Environments,” Proc. Int'l Symp. Computational Intelligence in Robotics and Automation, 1999.

[25]

S. Thrun, W. Burgard, and D. Fox, “A Real-Time Algorithm for Mobile Robot Mapping with Applications to Multi-Robot and 3D Mapping,” Proc. IEEE Int'l Conf. Robotics and Automation, 2000.

[26]

J. Neira, M.I. Ribeiro, and J.D. Tardos, “Mobile Robot Localisation and Map Building Using Monocular Vision,” Proc. Int'l Symp. Intelligent Robotics Systems, 1997.

[27]

A.J. Davison and D.W. Murray, “Mobile Robot Localisation Using Active Vision,” Proc. Fifth European Conf. Computer Vision, pp. 809-825, 1998.

Digital Library

[28]

A.J. Davison and D.W. Murray, “Simultaneous Localization and Map-Building Using Active Vision,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 865-880, July 2002.

Digital Library

[29]

A.J. Davison and N. Kita, “3D Simultaneous Localisation and Map-Building Using Active Vision for a Robot Moving on Undulating Terrain,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2001.

Digital Library

[30]

I. Jung and S. Lacroix, “High Resolution Terrain Mapping Using Low Altitude Aerial Stereo Imagery,” Proc. Ninth Int'l Conf. Computer Vision, 2003.

Digital Library

[31]

J.H. Kim and S. Sukkarieh, “Airborne Simultaneous Localisation and Map Building,” Proc. IEEE Int'l Conf. Robotics and Automation, pp. 406-411, 2003.

[32]

M. Bosse, R. Rikoski, J. Leonard, and S. Teller, “Vanishing Points and 3D Lines from Omnidirectional Video,” Proc. IEEE Int'l Conf. Image Processing, 2002.

[33]

R.M. Eustice, H. Singh, J.J. Leonard, M. Walter, and R. Ballard, “Visually Navigating the RMS Titanic with SLAM Information Filters,” Proc. Robotics: Science and Systems, 2005.

[34]

R. Sim, P. Elinas, M. Griffin, and J.J. Little, “Vision-Based SLAM Using the Rao-Blackwellised Particle Filter,” Proc. IJCAI Workshop Reasoning with Uncertainty in Robotics, 2005.

[35]

D.G. Lowe, “Object Recognition from Local Scale-Invariant Features,” Proc. Seventh Int'l Conf. Computer Vision, pp. 1150-1157, 1999.

Digital Library

[36]

N. Karlsson, E.D. Bernardo, J. Ostrowski, L. Goncalves, P. Pirjanian, and M.E. Munich, “The vSLAM Algorithm for Robust Localization and Mapping,” Proc. IEEE Int'l Conf. Robotics and Automation, 2005.

[37]

P.F. McLauchlan and D.W. Murray, “A Unifying Framework for Structure and Motion Recovery from Image Sequences,” Proc. Fifth Int'l Conf. Computer Vision, 1995.

Digital Library

[38]

A. Chiuso, P. Favaro, H. Jin, and S. Soatto, ““MFm”: 3-D Motion from 2-D Motion Causally Integrated over Time,” Proc. Sixth European Conf. Computer Vision, 2000.

Digital Library

[39]

D. Nistér, O. Naroditsky, and J. Bergen, “Visual Odometry,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2004.

[40]

E. Foxlin, “Generalized Architecture for Simultaneous Localization, Auto-Calibration and Map-Building,” Proc. IEEE/RSJ Conf. Intelligent Robots and Systems, 2002.

[41]

D. Burschka and G.D. Hager, “V-GPS(SLAM): Vision-Based Inertial System for Mobile Robots,” Proc. IEEE Int'l Conf. Robotics and Automation, 2004.

[42]

E. Eade and T. Drummond, “Scalable Monocular SLAM,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2006.

Digital Library

[43]

J. Shi and C. Tomasi, “Good Features to Track,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 593-600, 1994.

[44]

R. Swaminathan and S.K. Nayar, “Nonmetric Calibration of Wide-Angle Lenses and Polycameras,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 10, pp. 1172-1178, 2000.

Digital Library

[45]

A.J. Davison, “Active Search for Real-Time Vision,” Proc. 10th Int'l Conf. Computer Vision, 2005.

Digital Library

[46]

J. Solà, M. Devy, A. Monin, and T. Lemaire, “Undelayed Initialization in Bearing Only SLAM,” Proc. IEEE/RSJ Conf. Intelligent Robots and Systems, 2005.

[47]

J.M.M. Montiel, J. Civera, and A.J. Davison, “Unified Inverse Depth Parametrization for Monocular SLAM,” Proc. Robotics: Science and Systems, 2006.

Digital Library

[48]

H. Jin, P. Favaro, and S. Soatto, “A Semi-Direct Approach to Structure from Motion,” The Visual Computer, vol. 19, no. 6, pp.377-394, 2003.

Digital Library

[49]

N.D. Molton, A.J. Davison, and I.D. Reid, “Parameterisation and Probability in Image Alignment,” Proc. Asian Conf. Computer Vision, 2004.

[50]

S. Baker and I. Matthews, “Lucas-Kanade 20 Years on: A Unifying Framework: Part 1,” Int'l J. Computer Vision, vol. 56, no. 3, pp. 221-255, 2004.

Digital Library

[51]

V. Lepetit and P. Fua, “Monocular Model-Based 3D Tracking of Rigid Objects: A Survey,” Foundations and Trends in Computer Graphics and Vision, vol. 1, no. 1, pp. 1-89, Oct. 2005.

Digital Library

[52]

K. Kaneko, F. Kanehiro, S. Kajita, H. Hirukawa, T. Kawasaki, M. Hirata, K. Akachi, and T. Isozumi, “Humanoid Robot HRP-2,” Proc. IEEE Int'l Conf. Robotics and Automation, 2004.

[53]

Y. Takaoka, Y. Kida, S. Kagami, H. Mizoguchi, and T. Kanade, “3D Map Building for a Humanoid Robot by Using Visual Odometry,” Proc. IEEE Int'l Conf. Systems, Man, and Cybernetics, pp. 4444-4449, 2004.

[54]

K. Sabe, M. Fukuchi, J.-S. Gutmann, T. Ohashi, K. Kawamoto, and T. Yoshigahara, “Obstacle Avoidance and Path Planning for Humanoid Robots Using Stereo Vision,” Proc. IEEE Int'l Conf. Robotics and Automation, 2004.

[55]

P.M. Newman and K. Ho, “SLAM Loop-Closing with Visually Salient Features,” Proc. IEEE Int'l Conf. Robotics and Automation, 2005.

Cited By

Rojas-Perez LMartinez-Carranza JBicharra Garcia AFerro M(2024)Effective training to improve DeepPilotAI Communications10.3233/AIC-23006537:3(467-484)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/AIC-230065
Zhang YAn NShi CWang SWei HZhang PMeng XSun ZWang JLiang WTang FWu Y(2024)CID-SIMSInternational Journal of Robotics Research10.1177/0278364923122250743:7(899-917)Online publication date: 21-Jun-2024
https://dl.acm.org/doi/10.1177/02783649231222507
Zhang XZhou TWang JWang T(2024)Research on autonomous personnel localization methods with visual inertial fusionProceedings of the 2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence10.1145/3675417.3675553(816-823)Online publication date: 19-Jan-2024
https://dl.acm.org/doi/10.1145/3675417.3675553
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Index Terms

MonoSLAM: Real-Time Single Camera SLAM
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Robotics
      1. Robotic autonomy
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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cover image IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 29, Issue 6

June 2007

190 pages

ISSN:0162-8828

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Copyright © 2007.

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IEEE Computer Society

United States

Publication History

Published: 01 June 2007

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Cited By

Rojas-Perez LMartinez-Carranza JBicharra Garcia AFerro M(2024)Effective training to improve DeepPilotAI Communications10.3233/AIC-23006537:3(467-484)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/AIC-230065
Zhang YAn NShi CWang SWei HZhang PMeng XSun ZWang JLiang WTang FWu Y(2024)CID-SIMSInternational Journal of Robotics Research10.1177/0278364923122250743:7(899-917)Online publication date: 21-Jun-2024
https://dl.acm.org/doi/10.1177/02783649231222507
Zhang XZhou TWang JWang T(2024)Research on autonomous personnel localization methods with visual inertial fusionProceedings of the 2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence10.1145/3675417.3675553(816-823)Online publication date: 19-Jan-2024
https://dl.acm.org/doi/10.1145/3675417.3675553
Zhang HYang YZou Z(2024)ICON drone: Autonomous indoor exploration using Unmanned Aerial Vehicle for semantic 3D reconstructionProceedings of the 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3671127.3698172(66-76)Online publication date: 29-Oct-2024
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Ge YZhang LWu YHu D(2024)PIPO-SLAM: Lightweight Visual-Inertial SLAM With Preintegration Merging Theory and Pose-Only Descriptions of Multiple View GeometryIEEE Transactions on Robotics10.1109/TRO.2024.336681540(2046-2059)Online publication date: 1-Jan-2024
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Zuo YXu WWang XWang YKneip L(2024)Cross-Modal Semidense 6-DOF Tracking of an Event Camera in Challenging ConditionsIEEE Transactions on Robotics10.1109/TRO.2024.335537040(1600-1616)Online publication date: 1-Jan-2024
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Kaltiokallio OHostettler RGe YKim HTalvitie JWymeersch HValkama M(2024)A Multihypotheses Importance Density for SLAM in Cluttered ScenariosIEEE Transactions on Robotics10.1109/TRO.2023.333897540(1019-1035)Online publication date: 1-Jan-2024
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Wang KZhao GLu J(2024)A Deep Analysis of Visual SLAM Methods for Highly Automated and Autonomous Vehicles in Complex Urban EnvironmentIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.337999325:9(10524-10541)Online publication date: 4-Apr-2024
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