no. 4
Combining odometry and visual loop-closure detection for consistent topo-metrical mapping
RAIRO - Operations Research - Recherche Opérationnelle, Tome 44 (2010) no. 4, pp. 365-377.

We address the problem of simultaneous localization and mapping (SLAM) by combining visual loop-closure detection with metrical information given by a robot odometry. The proposed algorithm extends a purely appearance-based loop-closure detection method based on bags of visual words [A. Angeli, D. Filliat, S. Doncieux and J.-A. Meyer, IEEE Transactions On Robotics, Special Issue on Visual SLAM 24 (2008) 1027-1037], which is able to detect when the robot has returned back to a previously visited place. An efficient optimization algorithm is used to integrate odometry information and to generate a consistent topo-metrical map much more usable for global localization and path planning. The resulting algorithm which only requires a monocular camera and robot odometry data, is real-time, incremental (i.e. it does not require any a priori information on the environment), and can be easily embedded on medium platforms.

DOI : 10.1051/ro/2010021
Classification : 68T40, 93C85
Mots clés : SLAM, monocular vision, odometry, mobile robot, topo-metrical map 
@article{RO_2010__44_4_365_0,
     author = {Bazeille, S. and Filliat, D.},
     title = {Combining odometry and visual loop-closure detection for consistent topo-metrical mapping},
     journal = {RAIRO - Operations Research - Recherche Op\'erationnelle},
     pages = {365--377},
     publisher = {EDP-Sciences},
     volume = {44},
     number = {4},
     year = {2010},
     doi = {10.1051/ro/2010021},
     language = {en},
     url = {http://www.numdam.org/articles/10.1051/ro/2010021/}
}
TY  - JOUR
AU  - Bazeille, S.
AU  - Filliat, D.
TI  - Combining odometry and visual loop-closure detection for consistent topo-metrical mapping
JO  - RAIRO - Operations Research - Recherche Opérationnelle
PY  - 2010
SP  - 365
EP  - 377
VL  - 44
IS  - 4
PB  - EDP-Sciences
UR  - http://www.numdam.org/articles/10.1051/ro/2010021/
DO  - 10.1051/ro/2010021
LA  - en
ID  - RO_2010__44_4_365_0
ER  - 
%0 Journal Article
%A Bazeille, S.
%A Filliat, D.
%T Combining odometry and visual loop-closure detection for consistent topo-metrical mapping
%J RAIRO - Operations Research - Recherche Opérationnelle
%D 2010
%P 365-377
%V 44
%N 4
%I EDP-Sciences
%U http://www.numdam.org/articles/10.1051/ro/2010021/
%R 10.1051/ro/2010021
%G en
%F RO_2010__44_4_365_0
Bazeille, S.; Filliat, D. Combining odometry and visual loop-closure detection for consistent topo-metrical mapping. RAIRO - Operations Research - Recherche Opérationnelle, Tome 44 (2010) no. 4, pp. 365-377. doi : 10.1051/ro/2010021. http://www.numdam.org/articles/10.1051/ro/2010021/

[1] A. Angeli, D. Filliat, S. Doncieux and J.-A. Meyer, A fast and incremental method for loop-closure detection using bags of visual words, IEEE Transactions On Robotics, Special Issue on Visual SLAM 24 (2008) 1027-1037.

[2] T. Bailey and H. Durrant-Whyte, Simultaneous localisation and mapping (slam): Part ii. IEEE Robot. Autom. Mag. 13 (2006) 108-117.

[3] O. Booij, B. Terwijn, Z. Zivkovic and B. Kröse, Navigation using an appearance based topological map, in Proc. of the IEEE Int. Conf. on Robotics and Automation (2007).

[4] M. Cummins and P. Newman, Fab-map: Probabilistic localization and mapping in the space of appearance. Int. J. Robot. Res. 27 (2008) 647-665.

[5] A.J. Davison, I.D. Reid, N.D. Molton and O. Stasse, Monoslam: Real-time single camera slam. IEEE Trans. Pattern Anal. Mach. Intell. 29 (2007) 1052-1067.

[6] A. Diosi, A. Remazeilles, S. Segvic and F. Chaumette, Outdoor visual path following experiments, in Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS'07 (2007).

[7] T. Duckett, S. Marsland and J. Shapiro, Fast, on-line learning of globally consistent maps. Autonomous Robots 12 (2002) 287-300. | Zbl

[8] T. Duckett, S. Marsland and J. Shapiro, Learning globally consistent maps by relaxation, in Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA) (2000), pp. 3841-3846.

[9] E. Eade and T. Drummond, Monocular slam as a graph of coalesced observations, in Proc. of the Int. Conf. on Computer Vision (2007).

[10] D. Filliat, A visual bag of words method for interactive qualitative localization and mapping, in Proc. of the IEEE Int. Conf. on Robotics and Automation (2007).

[11] D. Filliat and J.A. Meyer, Global localization and topological map learning for robot navigation, in Proc. of the 7th Int. Conf. on Simulation of Adaptive Behavior (SAB02), From Animals to Animats 7 (2002).

[12] D. Filliat and J.-A. Meyer, Map-based navigation in mobile robots - I. A review of localisation strategies. J. Cogn. Systems Res. 4 (2003) 243-282.

[13] F. Fraundorfer, C. Engels and D. Nistér, Topological mapping, localization and navigation using image collections, in Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (2007).

[14] U. Frese, P. Larsson and T. Duckett, A multilevel relaxation algorithm for simultaneous localization and mapping. IEEE Trans. Robot. Autom. 21 (2005) 196-207.

[15] G. Grisetti, C. Stachniss, S. Grzonka and W. Burgard, A tree parameterization for efficiently computing maximum likelihood maps using gradient descent, in Proc. of Robotics: Science and Systems, Atlanta, GA, USA (2007).

[16] A. Howard, M.J. Mataric and G. Sukhatme, Relaxation on a mesh: a formalism for generalized localization, in Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (2001), pp. 1055-1060.

[17] K. Konolige, J. Bowman, J.D. Chen, P. Mihelich, M. Calonder, V. Lepetit and P. Fua, View-based maps, in Proc. of Robotics: Science and Systems, Seattle, USA (2009).

[18] K. Konolige and M. Agrawal, Frameslam: From bundle adjustment to real-time visual mapping. IEEE Trans. Robot. 24 (2008) 1066-1077.

[19] J. Kosecká, F. Li and X. Yang, Global localization and relative positioning based on scale-invariant keypoints. Robotics and Autonomous Systems 52 (2005) 209-228.

[20] D.G. Lowe, Distinctive image feature from scale-invariant keypoint. Int. J. Comp. Vis. 60 (2004) 91-110.

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

[22] M. Milford and G. Wyeth, Hippocampal models for simultaneous localisation and mapping on an autonomous robot, in Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA 2004) (2003).

[23] D. Nistér, An efficient solution to the five-point relative pose problem. IEEE Trans. Pattern Anal. Mach. Intell. 26 (2004) 756-777.

[24] D. Nistér, O. Naroditsky and J. Bergen, Visual odometry for ground vehicle applications. J. Field Robot. 23 (2006). | Zbl

[25] E. Olson, J. Leonard and S. Teller, Fast iterative alignment of pose graphs with poor initial estimates, in Proc. of the IEEE International Conference on Robotics and Automation (ICRA 2006) (2006), pp. 2262-2269.

[26] J.M. Porta and B.J.A. Kranse, Appearance-based concurrent map building and localization. Robot. Auton. Syst. 54 (2006) 159-164.

[27] P. Rybski, F. Zacharias, J. Lett, O. Masoud, M. Gini and N. Papanikolopoulos, Using visual features to build topological maps of indoor environments, in Proc. of the IEEE Int. Conf. on Robotics and Automation (2003).

[28] G. Sibley, C.r Mei, I. Reid and P. Newman, Adaptive relative bundle adjustment, in Robotics Science and Systems (RSS), Seattle, USA (2009).

[29] B. Steder, G. Grisetti, S. Grzonka, C. Stachniss, A. Rottmann and W. Burgard, Learning maps in 3d using attitude and noisy vision sensors, in Proc. of the IEEE/RSJ Int. Conf. on Intelligent RObots and Systems (2007).

[30] S. Thrun, W. Burgard and D. Fox, Probabilistic Robotics (Intelligent Robotics and Autonomous Agents). The MIT Press (2005). | Zbl

[31] E.C. Tolman, Cognitive maps in rats and men. Psychol. Rev. 55 (1948) 189-208.

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

Cité par Sources :