Online and Incremental Appearance-based SLAM in Highly Dynamic Environments

Abstract

In this paper we present a novel method for online and incremental appearance-based localization and mapping in a highly dynamic environment. Using position-invariant robust features (PIRFs), the method can achieve a high rate of recall with 100% precision. It can handle both strong perceptual aliasing and dynamic changes of places efficiently. Its performance also extends beyond conventional images; it is applicable to omnidirectional images for which the major portions of scenes are similar for most places. The proposed PIRF-based Navigation method named PIRF-Nav is evaluated by testing it on two standard datasets in a similar manner as in FAB-MAP and on an additional omnidirectional image dataset that we collected. This extra dataset was collected on 2 days with different specific events, i.e. an open-campus event, to present challenges related to illumination variance and strong dynamic changes, and to test assessment of dynamic scene changes. Results show that PIRF-Nav outperforms FAB-MAP; PIRF-Nav at precision-1 yields a recall rate about twice as high (approximately 80% increase) than that of FAB-MAP. Its computation time is sufficiently short for real-time applications. The method is fully incremental, and requires no offline process for dictionary creation. Additional testing using combined datasets proves that PIRF-Nav can function over the long term and can solve the kidnapped robot problem.

Keywords

Appearance-based localization and mapping invariant robust feature topological SLAM

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References

Angeli, A. , Filliat, D. , Doncieux, S. and Meyer, J. ( 2008). Fast and incremental method for loop-closure detection using bags of visual words. IEEE Transactions on Robotics , 24(2): 1027-1037.

Bay, H. , Tuytelaars, T. and Gool, L.V. ( 2006). SURF: Speeded Up Robust Features. Proceedings of the European Conference on Computer Vision (ECCV).

Bowling, M. , Wilkinson, D. , Ghodsi, A. and Milstein, A. ( 2005). Subjective localization with action respecting embedding . Proceedings International Symposium on Robotics Research (ISRR).

Chen, C. and Wang, H. ( 2006). Appearance-based topological bayesian inference for loop-closing detection in a cross country environment. The International Journal of Robotics Research, 25(10): 953-983.

Cummins, M. and Newman, P. ( 2008a). FAB-MAP: probabilistic localization and mapping in the space of appearance. The International Journal of Robotics Research, 27(6): 647-665.

Cummins, M. and Newman, P. ( 2008b). Accelerated appearance-only SLAM. Proceedings IEEE International Conference on Robotics and Automation (ICRA) .

Cummins, M. and Newman, P. ( 2009). Highly scalable appearance-only SLAM - FAB-MAP 2.0. Proceedings of Robotics: Science and Systems (RSS).

Filliat, D. and Meyer, J.A. ( 2003). Map-based navigation in mobile robots-I. A review of localisation strategies. Cognitive Systems Research, 4(4): 243-282.

Filliat, D. ( 2007). A visual bag of words method for interactive qualitative localization and mapping. Proceedings IEEE International Conference on Robotics and Automation (ICRA).

10.

Goedeme, T. , Nuttin, M. , Tuytelaars, T. and Gool, L.V. ( 2007). Omnidirectional vision based topological navigation. International Journal of Computer Vision, 74(3): 219-236.

11.

Ho, K. and Newman, P. ( 2007). Detecting loop closure with scene sequences. International Journal on Computer Vision, 74(3): 261-286.

12.

Kawewong, A. , Tangruamsub, S. and Hasegawa, O. ( 2009). Wide-baseline visible features for highly dynamic scene recognition. Proceedings of the International Conference on Computer Analysis of Images and Patterns (CAIP).

13.

Kosecka, J. , Li, F. and Yang, X. ( 2005). Global localization and relative positioning based on scale-invariant keypoints. Robotics and Autonomous Systems , 52(1): 27-38.

14.

Krose, B.J.A. , Vlassis, N.A. , Bunschoten, R. and Motomura, Y. ( 2001). A probabilistic model for appearance-based robot localization . Image and Vision Computing, 19(6): 381-391.

15.

Lamon, P. , Nourbakhsh, I. , Jensen, B. and Siegwart, R. ( 2001). Deriving and matching image fingerprint sequences for mobile robot localization. Proceedings IEEE International Conference on Robotics and Automation (ICRA).

16.

Levin, A. and Szeliski, R. ( 2004). Visual odometry and map correlation. Proceedings IEEE International Conference on Computer Vision and Pattern Recognition (CVPR).

17.

Li, F. and Kosecka, J. ( 2006). Probabilistic location recognition using reduced feature set. Proceedings of the International Conference on Robotics and Automation (ICRA).

18.

Lowe, D. ( 2004). Distinctive image features from scale-invariant keypoints . International Journal of Computer Vision, 60(2): 91-110.

19.

Meyer, J.A. and Filliat, D. ( 2003). Map-based navigation in mobile robots-III. A review of map-learning and path-planning strategies. Cognitive Systems Research, 4(4): 283-317.

20.

Newman, P. , Cole, D. and Ho, K. ( 2006). Outdoor SLAM using visual appearance and laser ranging . Proceedings IEEE International Conference on Robotics and Automation (ICRA).

21.

Nister, D. ( 2004) An efficient solution to the five-point relative pose problem. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(6): 756-777.

22.

Nister, D. and Stewenius, H. ( 2006) Scalable recognition with a vocabulary tree. Proceedings IEEE International Conference on Computer Vision and Pattern Recognition (CVPR).

23.

Schindler, G. , Brown, M. and Szeliski, R. ( 2007). City-scale location recognition. Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) .

24.

Se, S. , Lowe, D. and Little, J. ( 2001). Vision-based mobile robot localization and mapping using scale-invariant features. Proceedings IEEE International Conference on Robotics and Automation (ICRA).

25.

Silpa-Anan, C. and Hartley, R. ( 2004). Localisation using an Image-map. Proceedings of the Australian Conference on Robotics and Automation.

26.

Sivic, J. and Zisserman, A. ( 2003). Video Google: a text retrieval approach to object matching in videos. Proceedings IEEE International Conference on Computer Vision (ICCV).

27.

Torralba, A. , Murphy, K.P. , Freeman, W.T. and Rubin, M.A. ( 2003). Context-based vision system for place and object recognition . Proceedings IEEE International Conference on Computer Vision (ICCV).

28.

Ulrich, I. and Nourbakhsh, I. ( 2003). Appearance-based place recognition for topological localization . Proceedings IEEE International Conference on Robotics and Automation (ICRA).

29.

Wang, J. , Zha, H. and Cipolla, R. ( 2006). Coarse-to-fine vision-based localization by indexing scale-invariant features. IEEE Transactions on System, Man, and Cybernetics, 36(2): 413-422.

30.

Wolf, J. , Burgard, W. and Burkhardt, H. ( 2005). Robust vision-based localization by combining an image-retrieval system with Monte Carlo localization. IEEE Transactions on Robotics, 21(2): 208-216.

31.

Zivkovic, Z. , Bakker, B. and Krose, B. ( 2005). Hierarchical Map Building Using Visual Landmarks and Geometric Constraints. Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

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