Abstract
In this paper, we present an active SLAM solution with an active loop closing component which is independent on exploration component and at the same time allows high accuracy robot’s pose estimation and complete environment mapping. Inputs to our SLAM algorithm are RGBD image from the Kinect sensor and odometry estimates obtained from inertial measurement unit and wheel encoders. SLAM is based on the exactly sparse delayed state filter for real-time estimation of robot’s trajectory, vision-based pose registration, and loop closing. The active component ensures that localization remains accurate over a long period of time by sending the robot to close loops if a criterion function satisfies the predefined value. Our criterion function depends on the number of states predicted without an update between predictions, information gained from loop closing and the sheer distance between the loop closing state location and the current robot location. Once a state in which a loop closure should occur is reached and an update is performed, the robot returns to its previous goals. Since the active component is independent on the exploration part, the SLAM solution described in this paper can easily be merged with any existing exploration algorithm and the only requirement is that the exploration algorithm is able to stop exploration at any time and continue the exploration after the loop closing was accomplished. In this paper, we propose an active SLAM integration with the 2D laser range finder-based exploration algorithm that ensures the complete coverage of a polygonal environment and therefore a detailed mapping. The developed Active SLAM solution was verified through experiments which demonstrated its capability to work in real-time and to consistently map polygonal environments.
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References
J. Civera, O. G. Grasa, A. J. Davison, and J. Montiel, “1-Point RANSAC for extended Kalman filtering: Application to real-time structure from motion and visual odometry,” Journal of Field Robotics, vol. 27, no. 5, pp. 609–631, 2010.
M. Kaess, A. Ranganathan, and F. Dellaert, “isam: Incremental smoothing and mapping,” Robotics, IEEE Transactions on, vol. 24, no. 6, pp. 1365–1378, 2008.
U. Frese, “Efficient 6-dof slam with treemap as a generic backend,” in Robotics and Automation, 2007 IEEE International Conference on. IEEE, 2007, pp. 4814–4819.
G. Grisetti, C. Stachniss, and W. Burgard, “Improved techniques for grid mapping with rao-blackwellized particle filters,” Robotics, IEEE Transactions on, vol. 23, no. 1, pp. 34–46, 2007.
M. Montemerlo, S. Thrun, and B. Siciliano, FastSLAM: A scalable method for the simultaneous localization and mapping problem in robotics, ser. Springer Tracts in Advanced Robotics. Springer, 2007, vol. 27.
A. Kitanov and I. Petrović, “Generalization of 2d slam observability condition,” in 5th European Conference on Mobile Robots (ECMR2011), 2011.
A. A. Makarenko, S. B. Williams, F. Bourgault, and H. F. Durrant-Whyte, “An experiment in integrated exploration,” in IEEE / RSJ International Conference on Intelligent Robots and Systems, 2002, pp. 534–539.
S. Huang, N. Kwok, G. Dissanayake, Q. Ha, and G. Fang, “Multi-step look-ahead trajectory planning in slam: Possibility and necessity,” in IEEE / RSJ International Conference on Intelligent Robots and Systems, 2006, pp. 5026–5031.
R. Sim and N. Roy, “Global a-optimal robot exploration in slam,” in IEEE International Conference on Robotics and Automation, 2005, pp. 661–665.
C. Stachniss, G. Grisetti, and W. Burgard, “Information gain-based exploration using rao-blackwellized particle filters,” in Proceedings of Robotics: Science and Systems, 2005.
C. Leung, S. Huang, and G. Dissanayake, “Active slam using model predictive control and attractor based exploration,” in IEEE International Conference on Robotics and Automation, 2005, pp. 1091–1096.
T. Kollar and N. Roy, “Using reinforcement learning to improve exploration trajectories for error minimization,” in Proceedings of the IEEE International Conference on Robotics and Automation, 2006, pp. 3338–3343.
C. Stachniss, D. Hahnel, and W. Burgard, “Exploration with active loop-closing for fastslam,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004.
R. M. Eustice, H. Singh, and J. J. Leonard, “Exactly sparse delayed-state filters for view-based slam,” Robotics, IEEE Transactions on, vol. 22, no. 6, pp. 1100–1114, 2006.
R. Van Der Merwe, A. Doucet, N. De Freitas, and E. Wan, “The unscented particle filter,” in NIPS, 2000, pp. 584–590.
D. Borrmann, A. Nüchter, M. Dakulović, I. Maurović, I. Petrović, D. Osmanković, and J. Velagić, “The project thermalmapper - thermal 3d mapping of indoor environments for saving energy,” in Proceedings of the 10th International IFAC Symposium on Robot Control (SYROCO ’12), 2012.
A. Kitanov and I. Petrović, “Exactly sparse delayed state filter based robust slam with stereo vision,” in The joint conference of the 41st International Symposium on Robotics (ISR 2010) and the 6th German Conference on Robotics (ROBOTIK 2010), 2010.
M. Cummins and P. Newman, “Highly scalable appearance-only slam - fab-map 2.0.” in Robotics: Science and Systems. The MIT Press, 2009.
C. K. Chow and C. N. Liu, “Approximating Discrete Probability Distributions With Dependence Trees,” IEEE Transactions on Information Theory, vol. IT-14, pp. 462–467, 1968.
H. Bay, T. Tuytelaars, and L. Van Gool, “Surf: Speeded up robust features,” in In ECCV, 2006, pp. 404–417.
D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, pp. 91–110, 2004.
R. Cupec, E. K. Nyarko, D. Filko, and I. Petrović, “Fast pose tracking based on ranked 3D planar patch correspondences,” in 10th IFAC Symposium on Robot Control SYROCO, vol. 10, no. 1, 2012, pp. 108–113.
F. Schmitt and X. Chen, “Fast segmentation of range images into planar regions,” in Computer Vision and Pattern Recognition, 1991. Proceedings CVPR’91., IEEE Computer Society Conference on. IEEE, 1991, pp. 710–711.
M. A. Fischler and R. C. Bolles, “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography.” Commun. ACM, vol. 24, no. 6, pp. 381–395, 1981.
M. Dakulović, Š. Ileš, and I. Petrović, “Exploration and mapping of unknown polygonal environments based on uncertain range data,” Automatika, vol. 52, no. 2, pp. 118–131, 2011.
A. Ekman, A. Torne, and D. Stromberg, “Exploration of polygonal environments using range data.” Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 27, no. 2, pp. 250–255, 1997.
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This work has been supported by the European Community Seventh Framework Programme under grant No. 285939 (ACROSS).
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Lenac, K., Kitanov, A., Maurović, I., Dakulović, M., Petrović, I. (2016). Fast Active SLAM for Accurate and Complete Coverage Mapping of Unknown Environments. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_31
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