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NDT-Map-Code: A 3D global descriptor for real-time loop closure detection in lidar SLAM (2307.08221v2)

Published 17 Jul 2023 in cs.RO

Abstract: Loop-closure detection, also known as place recognition, aiming to identify previously visited locations, is an essential component of a SLAM system. Existing research on lidar-based loop closure heavily relies on dense point cloud and 360 FOV lidars. This paper proposes an out-of-the-box NDT (Normal Distribution Transform) based global descriptor, NDT-Map-Code, designed for both on-road driving and underground valet parking scenarios. NDT-Map-Code can be directly extracted from the NDT map without the need for a dense point cloud, resulting in excellent scalability and low maintenance cost. The NDT representation is leveraged to identify representative patterns, which are further encoded according to their spatial location (bearing, range, and height). Experimental results on the NIO underground parking lot dataset and the KITTI dataset demonstrate that our method achieves significantly better performance compared to the state-of-the-art.

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References (26)
  1. G. Kim and A. Kim, “Scan context: Egocentric spatial descriptor for place recognition within 3d point cloud map,” pp. 4802–4809, 2018.
  2. Y. Wang, Z. Sun, C.-Z. Xu, S. E. Sarma, J. Yang, and H. Kong, “Lidar iris for loop-closure detection,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2020, pp. 5769–5775.
  3. L. Li, X. Kong, X. Zhao, T. Huang, W. Li, F. Wen, H. Zhang, and Y. Liu, “Ssc: Semantic scan context for large-scale place recognition,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2021, pp. 2092–2099.
  4. H. Wang, C. Wang, and L. Xie, “Intensity scan context: Coding intensity and geometry relations for loop closure detection,” in 2020 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2020, pp. 2095–2101.
  5. P. Scovanner, S. Ali, and M. Shah, “A 3-dimensional sift descriptor and its application to action recognition,” in Proceedings of the 15th ACM international conference on Multimedia, 2007, pp. 357–360.
  6. Y. Cui, Y. Zhang, J. Dong, H. Sun, and F. Zhu, “Link3d: Linear keypoints representation for 3d lidar point cloud,” arXiv preprint arXiv:2206.05927, 2022.
  7. J. Knopp, M. Prasad, G. Willems, R. Timofte, and L. V. Gool, “Hough transform and 3d surf for robust three dimensional classification,” in European Conference on Computer Vision.   Springer, 2010, pp. 589–602.
  8. Y. Cui, X. Chen, Y. Zhang, J. Dong, Q. Wu, and F. Zhu, “Bow3d: Bag of words for real-time loop closing in 3d lidar slam,” IEEE Robotics and Automation Letters, 2022.
  9. S. Salti, F. Tombari, and L. Di Stefano, “Shot: Unique signatures of histograms for surface and texture description,” Computer Vision and Image Understanding, vol. 125, pp. 251–264, 2014.
  10. T. Shan, B. Englot, F. Duarte, C. Ratti, and D. Rus, “Robust place recognition using an imaging lidar,” in 2021 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2021, pp. 5469–5475.
  11. M. Bosse and R. Zlot, “Place recognition using keypoint voting in large 3d lidar datasets,” in 2013 IEEE International Conference on Robotics and Automation.   IEEE, 2013, pp. 2677–2684.
  12. B. Steder, M. Ruhnke, S. Grzonka, and W. Burgard, “Place recognition in 3d scans using a combination of bag of words and point feature based relative pose estimation,” in 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.   IEEE, 2011, pp. 1249–1255.
  13. K. Vidanapathirana, P. Moghadam, B. Harwood, M. Zhao, S. Sridharan, and C. Fookes, “Locus: Lidar-based place recognition using spatiotemporal higher-order pooling,” in 2021 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2021, pp. 5075–5081.
  14. L. He, X. Wang, and H. Zhang, “M2dp: A novel 3d point cloud descriptor and its application in loop closure detection,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2016, pp. 231–237.
  15. R. Zhou, L. He, H. Zhang, X. Lin, and Y. Guan, “Ndd: A 3d point cloud descriptor based on normal distribution for loop closure detection,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2022, pp. 1328–1335.
  16. J. Ma, J. Zhang, J. Xu, R. Ai, W. Gu, and X. Chen, “Overlaptransformer: An efficient and yaw-angle-invariant transformer network for lidar-based place recognition,” IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 6958–6965, 2022.
  17. A. Dewan, T. Caselitz, and W. Burgard, “Learning a local feature descriptor for 3d lidar scans,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2018, pp. 4774–4780.
  18. H. Yin, X. Ding, L. Tang, Y. Wang, and R. Xiong, “Efficient 3d lidar based loop closing using deep neural network,” in 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO).   IEEE, 2017, pp. 481–486.
  19. R. Dube, A. Cramariuc, D. Dugas, H. Sommer, M. Dymczyk, J. Nieto, R. Siegwart, and C. Cadena, “Segmap: Segment-based mapping and localization using data-driven descriptors,” The International Journal of Robotics Research, vol. 39, no. 2-3, pp. 339–355, 2020.
  20. M. A. Uy and G. H. Lee, “Pointnetvlad: Deep point cloud based retrieval for large-scale place recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 4470–4479.
  21. R. Arandjelovic, P. Gronat, A. Torii, T. Pajdla, and J. Sivic, “Netvlad: Cnn architecture for weakly supervised place recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 5297–5307.
  22. Z. Zhou, C. Zhao, D. Adolfsson, S. Su, Y. Gao, T. Duckett, and L. Sun, “Ndt-transformer: Large-scale 3d point cloud localisation using the normal distribution transform representation,” in 2021 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2021, pp. 5654–5660.
  23. A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” The International Journal of Robotics Research, vol. 32, no. 11, pp. 1231–1237, 2013.
  24. C. Yuan, J. Lin, Z. Zou, X. Hong, and F. Zhang, “Std: Stable triangle descriptor for 3d place recognition,” in 2023 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2023, pp. 1897–1903.
  25. T. Stoyanov, M. Magnusson, H. Andreasson, and A. J. Lilienthal, “Fast and accurate scan registration through minimization of the distance between compact 3d ndt representations,” The International Journal of Robotics Research, vol. 31, no. 12, pp. 1377–1393, 2012.
  26. T. Shan, B. Englot, D. Meyers, W. Wang, C. Ratti, and R. Daniela, “Lio-sam: Tightly-coupled lidar inertial odometry via smoothing and mapping,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2020, pp. 5135–5142.
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