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Combining Odometry and Visual Loop-Closure Detection for Consistent Topo-Metrical Mapping

Published online by Cambridge University Press:  11 January 2011

S. Bazeille  and
D. Filliat
S. Bazeille
Affiliation:
ENSTA ParisTech, Unité Électronique et Informatique – 32 Boulevard Victor, 75015 Paris, France. stephane.bazeille@ensta.fr; david.filliat@ensta.fr
D. Filliat
Affiliation:
ENSTA ParisTech, Unité Électronique et Informatique – 32 Boulevard Victor, 75015 Paris, France. stephane.bazeille@ensta.fr; david.filliat@ensta.fr
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Abstract

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.

Keywords

SLAMmonocular visionodometrymobile robottopo-metrical map

Information

Type
Research Article
Information
RAIRO - Operations Research , Volume 44 , Issue 4: COGIS'09 , October 2010 , pp. 365 - 377
Copyright
© EDP Sciences, ROADEF, SMAI, 2011

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References

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.
Bailey, T. and Durrant-Whyte, H., Simultaneous localisation and mapping (slam): Part ii. IEEE Robot. Autom. Mag. 13 (2006) 108–117. CrossRef
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).
Cummins, M. and Newman, P., Fab-map: Probabilistic localization and mapping in the space of appearance. Int. J. Robot. Res. 27 (2008) 647–665. CrossRef
Davison, A.J., Reid, I.D., Molton, N.D. and Stasse, O., Monoslam: Real-time single camera slam. IEEE Trans. Pattern Anal. Mach. Intell. 29 (2007) 1052–1067. CrossRef
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).
Duckett, T., Marsland, S. and Shapiro, J., Fast, on-line learning of globally consistent maps. Autonomous Robots 12 (2002) 287–300. CrossRef
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.
E. Eade and T. Drummond, Monocular slam as a graph of coalesced observations, in Proc. of the Int. Conf. on Computer Vision (2007).
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).
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).
Filliat, D. and Meyer, J.-A., Map-based navigation in mobile robots – I. A review of localisation strategies. J. Cogn. Systems Res. 4 (2003) 243–282. CrossRef
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).
Frese, U., Larsson, P. and Duckett, T., A multilevel relaxation algorithm for simultaneous localization and mapping. IEEE Trans. Robot. Autom. 21 (2005) 196–207. CrossRef
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).
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.
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).
Konolige, K. and Agrawal, M., Frameslam: From bundle adjustment to real-time visual mapping. IEEE Trans. Robot. 24 (2008) 1066–1077. CrossRef
Kosecká, J., Li, F. and Yang, X., Global localization and relative positioning based on scale-invariant keypoints. Robotics and Autonomous Systems 52 (2005) 209–228. CrossRef
Lowe, D.G., Distinctive image feature from scale-invariant keypoint. Int. J. Comp. Vis. 60 (2004) 91–110. CrossRef
Menegatti, E., Zoccarato, M., Pagello, E. and Ishiguro, H., Image-based monte-carlo localisation with omnidirectional images. Robot. Auton. Syst. 48 (2004) 17–30. CrossRef
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).
Nistér, D., An efficient solution to the five-point relative pose problem. IEEE Trans. Pattern Anal. Mach. Intell. 26 (2004) 756–777. CrossRef
D. Nistér, O. Naroditsky and J. Bergen, Visual odometry for ground vehicle applications. J. Field Robot. 23 (2006).
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.
Porta, J.M. and Kranse, B.J.A., Appearance-based concurrent map building and localization. Robot. Auton. Syst. 54 (2006) 159–164. CrossRef
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).
G. Sibley, C.r Mei, I. Reid and P. Newman, Adaptive relative bundle adjustment, in Robotics Science and Systems (RSS), Seattle, USA (2009).
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).
S. Thrun, W. Burgard and D. Fox, Probabilistic Robotics (Intelligent Robotics and Autonomous Agents). The MIT Press (2005).
Tolman, E.C., Cognitive maps in rats and men. Psychol. Rev. 55 (1948) 189–208. CrossRef
Wang, J., Zha, H. and Cipolla, R., Coarse-to-fine vision-based localization by indexing scale-invariant features. IEEE Trans. Syst. Man Cybern. 36 (2006) 413–422. CrossRef