Combining Odometry and Visual Loop-Closure Detection for Consistent Topo-Metrical Mapping
RAIRO - Operations Research (2011)
- Volume: 44, Issue: 4, page 365-377
- ISSN: 0399-0559
Access Full Article
topAbstract
topHow to cite
topBazeille, S., and Filliat, D.. "Combining Odometry and Visual Loop-Closure Detection for Consistent Topo-Metrical Mapping." RAIRO - Operations Research 44.4 (2011): 365-377. <http://eudml.org/doc/44698>.
@article{Bazeille2011,
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 SLAM24 (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.
},
author = {Bazeille, S., Filliat, D.},
journal = {RAIRO - Operations Research},
keywords = {SLAM; monocular vision; odometry; mobile robot; topo-metrical map},
language = {eng},
month = {1},
number = {4},
pages = {365-377},
publisher = {EDP Sciences},
title = {Combining Odometry and Visual Loop-Closure Detection for Consistent Topo-Metrical Mapping},
url = {http://eudml.org/doc/44698},
volume = {44},
year = {2011},
}
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
DA - 2011/1//
PB - EDP Sciences
VL - 44
IS - 4
SP - 365
EP - 377
AB -
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 SLAM24 (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.
LA - eng
KW - SLAM; monocular vision; odometry; mobile robot; topo-metrical map
UR - http://eudml.org/doc/44698
ER -
References
top- 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 SLAM24 (2008) 1027–1037.
- T. Bailey and H. Durrant-Whyte, Simultaneous localisation and mapping (slam): Part ii. IEEE Robot. Autom. Mag.13 (2006) 108–117.
- 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).
- M. Cummins and P. Newman, Fab-map: Probabilistic localization and mapping in the space of appearance. Int. J. Robot. Res.27 (2008) 647–665.
- 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.
- 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).
- T. Duckett, S. Marsland and J. Shapiro, Fast, on-line learning of globally consistent maps. Autonomous Robots12 (2002) 287–300.
- 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).
- 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.
- 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).
- U. Frese, P. Larsson and T. Duckett, A multilevel relaxation algorithm for simultaneous localization and mapping. IEEE Trans. Robot. Autom.21 (2005) 196–207.
- 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).
- K. Konolige and M. Agrawal, Frameslam: From bundle adjustment to real-time visual mapping. IEEE Trans. Robot.24 (2008) 1066–1077.
- J. Kosecká, F. Li and X. Yang, Global localization and relative positioning based on scale-invariant keypoints. Robotics and Autonomous Systems52 (2005) 209–228.
- D.G. Lowe, Distinctive image feature from scale-invariant keypoint. Int. J. Comp. Vis.60 (2004) 91–110.
- E. Menegatti, M. Zoccarato, E. Pagello and H. Ishiguro, Image-based monte-carlo localisation with omnidirectional images. Robot. Auton. Syst.48 (2004) 17–30.
- 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).
- D. Nistér, An efficient solution to the five-point relative pose problem. IEEE Trans. Pattern Anal. Mach. Intell.26 (2004) 756–777.
- 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.
- J.M. Porta and B.J.A. Kranse, Appearance-based concurrent map building and localization. Robot. Auton. Syst.54 (2006) 159–164.
- 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).
- E.C. Tolman, Cognitive maps in rats and men. Psychol. Rev.55 (1948) 189–208.
- 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.
NotesEmbed ?
topTo embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.