Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
139 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Lighthouses and Global Graph Stabilization: Active SLAM for Low-compute, Narrow-FoV Robots (2306.10463v1)

Published 18 Jun 2023 in cs.RO

Abstract: Autonomous exploration to build a map of an unknown environment is a fundamental robotics problem. However, the quality of the map directly influences the quality of subsequent robot operation. Instability in a simultaneous localization and mapping (SLAM) system can lead to poorquality maps and subsequent navigation failures during or after exploration. This becomes particularly noticeable in consumer robotics, where compute budget and limited field-of-view are very common. In this work, we propose (i) the concept of lighthouses: panoramic views with high visual information content that can be used to maintain the stability of the map locally in their neighborhoods and (ii) the final stabilization strategy for global pose graph stabilization. We call our novel exploration strategy SLAM-aware exploration (SAE) and evaluate its performance on real-world home environments.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (30)
  1. A. Bircher, M. Kamel, K. Alexis, H. Oleynikova, and R. Siegwart, “Receding horizon” next-best-view” planner for 3d exploration,” in 2016 IEEE international conference on robotics and automation (ICRA).   IEEE, 2016, pp. 1462–1468.
  2. T. Dang, F. Mascarich, S. Khattak, C. Papachristos, and K. Alexis, “Graph-based path planning for autonomous robotic exploration in subterranean environments,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2019, pp. 3105–3112.
  3. M. Dharmadhikari, T. Dang, L. Solanka, J. Loje, H. Nguyen, N. Khedekar, and K. Alexis, “Motion primitives-based path planning for fast and agile exploration using aerial robots,” in 2020 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2020, pp. 179–185.
  4. B. Yamauchi, “A frontier-based approach for autonomous exploration,” in Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA’97.’Towards New Computational Principles for Robotics and Automation’.   IEEE, 1997, pp. 146–151.
  5. G. Grisetti, R. Kümmerle, C. Stachniss, and W. Burgard, “A tutorial on graph-based slam,” IEEE Intelligent Transportation Systems Magazine, vol. 2, no. 4, pp. 31–43, 2010.
  6. J. Hu, H. Niu, J. Carrasco, B. Lennox, and F. Arvin, “Voronoi-based multi-robot autonomous exploration in unknown environments via deep reinforcement learning,” IEEE Transactions on Vehicular Technology, vol. 69, no. 12, pp. 14 413–14 423, 2020.
  7. M. Ramezani, G. Tinchev, E. Iuganov, and M. Fallon, “Online lidar-slam for legged robots with robust registration and deep-learned loop closure,” in 2020 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2020, pp. 4158–4164.
  8. C. Stachniss, D. Hahnel, and W. Burgard, “Exploration with active loop-closing for fastslam,” in 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)(IEEE Cat. No. 04CH37566), vol. 2.   IEEE, 2004, pp. 1505–1510.
  9. H. Lehner, M. J. Schuster, T. Bodenmüller, and S. Kriegel, “Exploration with active loop closing: A trade-off between exploration efficiency and map quality,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2017, pp. 6191–6198.
  10. J. A. Placed, J. Strader, H. Carrillo, N. Atanasov, V. Indelman, L. Carlone, and J. A. Castellanos, “A survey on active simultaneous localization and mapping: State of the art and new frontiers,” arXiv preprint arXiv:2207.00254, 2022.
  11. H. H. González-Banos and J.-C. Latombe, “Navigation strategies for exploring indoor environments,” The International Journal of Robotics Research, vol. 21, no. 10-11, pp. 829–848, 2002.
  12. B. Tovar, L. Munoz-Gómez, R. Murrieta-Cid, M. Alencastre-Miranda, R. Monroy, and S. Hutchinson, “Planning exploration strategies for simultaneous localization and mapping,” Robotics and Autonomous Systems, vol. 54, no. 4, pp. 314–331, 2006.
  13. M. Keidar and G. A. Kaminka, “Robot exploration with fast frontier detection: Theory and experiments,” in Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems-Volume 1, 2012, pp. 113–120.
  14. P. Quin, D. D. K. Nguyen, T. L. Vu, A. Alempijevic, and G. Paul, “Approaches for efficiently detecting frontier cells in robotics exploration,” Frontiers in Robotics and AI, vol. 8, p. 616470, 2021.
  15. D. Holz, N. Basilico, F. Amigoni, and S. Behnke, “Evaluating the efficiency of frontier-based exploration strategies,” in ISR 2010 (41st International Symposium on Robotics) and ROBOTIK 2010 (6th German Conference on Robotics).   VDE, 2010, pp. 1–8.
  16. M. Keidar and G. A. Kaminka, “Efficient frontier detection for robot exploration,” The International Journal of Robotics Research, vol. 33, no. 2, pp. 215–236, 2014.
  17. C.-Y. Wu and H.-Y. Lin, “Autonomous mobile robot exploration in unknown indoor environments based on rapidly-exploring random tree,” in 2019 IEEE International Conference on Industrial Technology (ICIT).   IEEE, 2019, pp. 1345–1350.
  18. E. Bonetto, P. Goldschmid, M. Pabst, M. J. Black, and A. Ahmad, “irotate: Active visual slam for omnidirectional robots,” Robotics and Autonomous Systems, vol. 154, p. 104102, 2022.
  19. J. A. Placed and J. A. Castellanos, “A deep reinforcement learning approach for active slam,” Applied Sciences, vol. 10, no. 23, p. 8386, 2020.
  20. S. Suresh, P. Sodhi, J. G. Mangelson, D. Wettergreen, and M. Kaess, “Active slam using 3d submap saliency for underwater volumetric exploration,” in 2020 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2020, pp. 3132–3138.
  21. J. A. Placed, J. J. G. Rodríguez, J. D. Tardós, and J. A. Castellanos, “Explorb-slam: Active visual slam exploiting the pose-graph topology,” in ROBOT2022: Fifth Iberian Robotics Conference: Advances in Robotics, Volume 1.   Springer, 2022, pp. 199–210.
  22. A. Kim and R. M. Eustice, “Active visual slam for robotic area coverage: Theory and experiment,” The International Journal of Robotics Research, vol. 34, no. 4-5, pp. 457–475, 2015.
  23. Y. Chen, S. Huang, and R. Fitch, “Active slam for mobile robots with area coverage and obstacle avoidance,” IEEE/ASME Transactions on Mechatronics, vol. 25, no. 3, pp. 1182–1192, 2020.
  24. C. Leung, S. Huang, and G. Dissanayake, “Active slam using model predictive control and attractor based exploration,” in 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.   IEEE, 2006, pp. 5026–5031.
  25. L. Tai and M. Liu, “Mobile robots exploration through cnn-based reinforcement learning,” Robotics and biomimetics, vol. 3, no. 1, pp. 1–8, 2016.
  26. O. Zhelo, J. Zhang, L. Tai, M. Liu, and W. Burgard, “Curiosity-driven exploration for mapless navigation with deep reinforcement learning,” arXiv preprint arXiv:1804.00456, 2018.
  27. C. Oh and A. Cavallaro, “Learning action representations for self-supervised visual exploration,” in 2019 International Conference on Robotics and Automation (ICRA).   IEEE, 2019, pp. 5873–5879.
  28. F. Dellaert, M. Kaess, et al., “Factor graphs for robot perception,” Foundations and Trends® in Robotics, vol. 6, no. 1-2, pp. 1–139, 2017.
  29. C. B. Barber, D. P. Dobkin, and H. Huhdanpaa, “The quickhull algorithm for convex hulls,” ACM Transactions on Mathematical Software (TOMS), vol. 22, no. 4, pp. 469–483, 1996.
  30. H. Liu, M. Chen, G. Zhang, H. Bao, and Y. Bao, “Ice-ba: Incremental, consistent and efficient bundle adjustment for visual-inertial slam,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 1974–1982.
Citations (2)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now