Robotic systems that can create and use visual maps in real-time have obvious advantages in many applications, from automatic driving to mobile manipulation in the home. In this paper we describe a mapping system based on retaining stereo views of the environment that are collected as the robot moves. Connections among the views are formed by consistent geometric matching of their features. Out-of-sequence matching is the key problem: how to find connections from the current view to other corresponding views in the map. Our approach uses a vocabulary tree to propose candidate views, and a strong geometric filter to eliminate false positives: essentially, the robot continually re-recognizes where it is. We present experiments showing the utility of the approach on video data, including incremental map building in large indoor and outdoor environments, map building without localization, and re-localization when lost.
Agrawal, M. and Konolige, K. ( 2008a). CenSurE: center surround extremas for realtime feature detection and matching. European Conference on Computer Vision.
2.
Agrawal, M. and Konolige, K. ( 2008b). FrameSLAM: from bundle adjustment to real-time visual mapping. IEEE Transactions on Robotics, 24(5): 1066-1077.
3.
Amit, Y. and Geman, D. ( 1997). Shape quantization and recognition with randomized trees . Neural Computation, 9(7): 1545-1588.
4.
Bay, H., Ess, A., Tuytelaars, T. and Gool, L.V. ( 2008). SURF: speeded up robust features. Computer Vision and Image Understanding, 10(3): 346-359.
5.
Boiman, O., Shechtman, E. and Irani, M. ( 2008). defense of nearest-neighbor based image classification . Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, IEEE Press.
6.
Callmer, J., Granström, K., Nieto, J. and Ramos, F. ( 2008). Tree of words for visual loop closure detection in urban SLAM. Proceedings of the 2008 Australasian Conference on Robotics and Automation.
7.
Calonder, M., Lepetit, V. and Fua, P. ( 2008a). Keypoint signatures for fast learning and recognition . European Conference on Computer Vision.
8.
Calonder, M., Lepetit, V. and Fua, P. ( 2008b). Keypoint signatures for fast learning and recognition . European Conference on Computer Vision, Marseille, France.
9.
Calonder, M., Lepetit, V., Konolige, K., Bowman, J., Mihelich, P. and Fua, P. ( 2009). Compact signatures for high-speed interest point description and matching. International Conference on Computer Vision, Kyoto, Japan.
10.
Comport, A., Malis, E., and Rives, P. ( 2007). Accurate quadri-focal tracking for robust 3d visual odometry . International Conference on Robotics and Automation .
11.
Cummins, M. and Newman, P.M. ( 2007). Probabilistic appearance based navigation and loop closing . International Conference on Robotics and Automation .
12.
Cummins, M. and Newman, P.M. ( 2009). Highly scalable appearance-only SLAM-FAB-MAP 2.0. Proceedings of Robotics Science and Systems.
13.
Davison, A. ( 2003). Real-time simultaneaous localisation and mapping with a single camera. International Conference on Computer Vision, pp. 1403-1410.
14.
Davison, A.J., Reid, I.D., Molton, N.D. and Stasse, O. ( 2007). MonoSLAM: Real-time single camera SLAM. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6): 1052-1067.
15.
Dellaert, F. ( 2005). Square Root SAM: simultaneous location and mapping via square root information smoothing. Proceedings of Robotics Science and Systems.
16.
Eade, E. and Drummond, T. ( 2007). Monocular SLAM as a graph of coalesced observations. International Conference on Computer Vision.
17.
Eade, E. and Drummond, T. ( 2008). Unified loop closing and recovery for real time monocular SLAM. British Machine Vision Conference.
18.
Eustice, R.M., Singh, H., Leonard, J.J. and Walter, M.R. ( 2006). Visually mapping the RMS Titanic: conservative covariance estimates for SLAM information filters. The International Journal of Robotics Research, 25(12): 1223- 1242.
19.
Fraundorfer, F., Engels, C. and Nistér, D. ( 2007). Topological mapping, localization and navigation using image collections. International Conference on Intelligent Robots and Systems, pp. 3872-3877.
20.
Grisetti, G., Stachniss, C., Grzonka, S. and Burgard, W. ( 2007). A tree parameterization for efficiently computing maximum likelihood maps using gradient descent. Proceedings of Robotics Science and Systems.
21.
Gutmann, J. and Konolige, K. ( 1999). Incremental mapping of large cyclic environments. Proceedings IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA), Monterey, CA, pp. 318-325.
22.
Jegou, H., Douze, M. and Schmid, C. ( 2008). Hamming embedding and weak geometric consistency for large scale image search. European Conference on Computer Vision.
23.
Jegou, H., Harzallah, H. and Schmid, C. ( 2007). A contextual dissimilarity measure for accurate and efficient image search. Computer Vision and Pattern Recognition (CVPR), pp. 1-8.
24.
Kaess, M., Ranganathan, A. and Dellaert, F. ( 2007). iSAM: fast incremental smoothing and mapping with efficient data association. International Conference on Robotics and Automation, Rome.
25.
Kelly, A. and Unnikrishnan, R. ( 2003). Efficient construction of globally consistent ladar maps using pose network topology and nonlinear programming. Proceedings 11th International Symposium of Robotics Research.
26.
Klein, G. and Murray, D. ( 2007). Parallel tracking and mapping for small AR workspaces . Proceedings of the Sixth IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR’07), Nara, Japan.
27.
Klein, G. and Murray, D. ( 2008). Improving the agility of keyframe-based SLAM. European Conference on Computer Vision.
28.
Klopschitz, M., Zach, C., Irschara, A. and Schmalstieg, D. ( 2008). Generalized detection and merging of loop closures for video sequences. Proceedings of 3D Data Processing, Visualization, and Transmission.
29.
Konolige, K. and Agrawal, M. ( 2007). Frame-frame matching for realtime consistent visual mapping . International Conference on Robotics and Automation .
30.
Konolige, K., Agrawal, M. and Solà, J. ( 2007). Large scale visual odometry for rough terrain. Proceedings of the International Symposium on Research in Robotics (ISRR).
31.
Konolige, K., Bowman, J., Chen, J., Mihelich, P., Colander, M., Lepetit, V. and Fua, P. ( 2009). View-based maps. Proceedings of Robotics Science and Systems.
32.
Lepetit, V. and Fua, P. ( 2006). Keypoint recognition using randomized trees. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(9): 1465-1479.
33.
Lowe, D. ( 2004). Distinctive Image Features from Scale-Invariant Keypoints . International Journal of Computer Vision, 20(2): 91-110.
34.
Lu, F. and Milios, E. ( 1997). Globally consistent range scan alignment for environment mapping. Autonomous Robots, 4: 333-349.
35.
Mahon, I., Williams, S., Pizarro, O. and Johnson-Roberson, M. ( 2008). Efficient view-based SLAM using visual loop closures . IEEE Transactions on Robotics, 24(5): 1002-1014.
36.
Mei, C., Sibley, G., Cummins, M., Newman, P. and Reid, I. ( 2009). A constant time efficient stereo SLAM system. British Machine Vision Conference.
37.
Muja, M. and Lowe, D. ( 2009). Fast approximate nearest neighbors with automatic algorithm configuration. Proceedings of VISAPP.
38.
Newman, P., Sibley, G., Smith, M., Cummins, M., Harrison, A., Mei, C., Posner, I., Shade, R., Schroeter, D., Murphy, L., Churchill, W., Cole, D. and Reid, I. ( 2009). Navigating, recognizing and describing urban spaces with vision and lasers. The International Journal of Robotics Research , 28(11-12): 1406-1433.
39.
Nistér, D. and Stewénius, H. ( 2006). Scalable recognition with a vocabulary tree. Proceedings of CVPR.
40.
Ozuysal, M., Calonder, M., Lepetit, V. and Fua, P. ( 2008). Fast Keypoint Online Learning and Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 32(3): 448-461.
41.
Paz, L., Tardós, J. and Neira, J. ( 2008). Divide and conquer: EKF SLAM in O(n). IEEE Transactions on Robotics, 24(5): 1107-1120.
42.
Rosten, E. and Drummond, T. ( 2006). Machine learning for high-speed corner detection. European Conference on Computer Vision, Vol. 1. http://mi.eng.cam.ac.uk/∼er258/work/rosten_2006_machine.pdf.
43.
Sibley, G., Mei, C., Reid, I. and Newman, P. ( 2009). Adaptive relative bundle adjustment. Proceedings of Robotics Science and Systems, Seattle, WA .
44.
Sivic, J. and Zisserman, A. ( 2003). Video Google: a text retrieval approach to object matching in videos. International Conference on Computer Vision, p. 1470.
45.
Snavely, N., Seitz, S.M. and Szeliski, R. ( 2008). Skeletal sets for efficient structure from motion. Proceedings of Computer Vision and Pattern Recognition.
46.
Solà, J., Devy, M., Monin, A. and Lemaire, T. ( 2005). Undelayed initialization in bearing only SLAM. International Conference on Robotics and Automation.
47.
Steder, B., Grisetti, G., Stachniss, C., Grzonka, S., Rottmann, A. and Burgard, W. ( 2007). Learning maps in 3D using attitude and noisy vision sensors . International Conference on Intelligent Robots and Systems.
48.
Thrun, S. and Montemerlo, M. ( 2006). The graph SLAM algorithm with applications to large-scale mapping of urban structures. The International Journal of Robotics Research, 25(5-6): 403-429.
49.
Unnikrishnan, R. and Kelly, A. ( 2002). A constrained optimization approach to globally consistent mapping. International Conference on Intelligent Robots and Systems.
50.
Williams, B., Klein, G. and Reid, I. ( 2007). Real-time SLAM relocalisation. International Conference on Computer Vision.
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