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Semantic geometric fusion multi-object tracking and lidar odometry in dynamic environment

Published online by Cambridge University Press:  11 January 2024

Tingchen Ma ,
Guolai Jiang ,
Yongsheng Ou Open the ORCID record for Yongsheng Ou [Opens in a new window]  and
Sheng Xu
Tingchen Ma
Affiliation:
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, 518055, China
Guolai Jiang
Affiliation:
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
Yongsheng Ou*
Affiliation:
Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China
Sheng Xu*
Affiliation:
Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
*
Corresponding authors: Yongsheng Ou, Sheng Xu; Emails: yoo2023@dlut.edu.cn, sheng.xu@siat.ac.cn
Corresponding authors: Yongsheng Ou, Sheng Xu; Emails: yoo2023@dlut.edu.cn, sheng.xu@siat.ac.cn
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Abstract

Simultaneous localization and mapping systems based on rigid scene assumptions cannot achieve reliable positioning and mapping in a complex environment with many moving objects. To solve this problem, this paper proposes a novel dynamic multi-object lidar odometry (MLO) system based on semantic object recognition technology. The proposed system enables the reliable localization of robots and semantic objects and the generation of long-term static maps in complex dynamic scenes. For ego-motion estimation, the proposed system extracts environmental features that take into account both semantic and geometric consistency constraints. Then, the filtered features can be robust to the semantic movable and unknown dynamic objects. In addition, we propose a new least-squares estimator that uses geometric object points and semantic box planes to realize the multi-object tracking (SGF-MOT) task robustly and precisely. In the mapping module, we implement dynamic semantic object detection using the absolute trajectory tracking list. By using static semantic objects and environmental features, the system eliminates accumulated localization errors and produces a purely static map. Experiments on the public KITTI dataset show that the proposed MLO system provides more accurate and robust object tracking performance and better real-time localization accuracy in complex scenes compared to existing technologies.

Keywords

mobile robotsnavigationlidar SLAMmulti-object trackingdynamic object detection
Type
Research Article
Information
Robotica , Volume 42 , Issue 3 , March 2024 , pp. 891 - 910
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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References

Zhang, J. and Singh, S., “Loam: Lidar odometry and mapping in real-time,” Rob. Sci. Syst. 2(9), 1–9 (2014).Google Scholar
Shan, T. and Englot, B., “Lego-loam: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain,” 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2018) pp. 4758–4765.Google Scholar
Mur-Artal, R., Montiel, J. M. M. and Tardos, J. D., “ORB-SLAM: A versatile and accurate monocular slam system,” IEEE Trans. Rob. 31(5), 1147–1163 (2015).Google Scholar
Behley, J. and Stachniss, C., “Efficient surfel-based slam using 3d laser range data in urban environments,” Rob. Sci. Syst. 2018(1), 59 (2018).Google Scholar
Deschaud, J.-E., “IMLS-SLAM: Scan-to-Model Matching Based on 3d Data,” 2018 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2018) pp. 2480–2485.Google Scholar
Moosmann, F. and Fraichard, T., “Motion Estimation from Range Images in Dynamic Outdoor Scenes,” 2010 IEEE International Conference on Robotics and Automation (IEEE, 2010) pp. 142–147.Google Scholar
Park, J., Cho, Y. and Shin, Y.-S., “Nonparametric background model-based lidar slam in highly dynamic urban environments,” IEEE Trans. Intell. Transp. 23(12), 24190–24205 (2022).Google Scholar
Tan, W., Liu, H., Dong, Z., Zhang, G. and Bao, H., “Robust Monocular SLAM in Dynamic Environments,” 2013 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) (IEEE, 2013) pp. 209–218.Google Scholar
Ruchti, P. and Burgard, W., “Mapping with Dynamic-Object Probabilities Calculated from Single 3d Range Scans,” 2018 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2018) pp. 6331–6336.Google Scholar
Pfreundschuh, P., Hendrikx, H. F., Reijgwart, V., Dubé, R., Siegwart, R. and Cramariuc, A., “Dynamic Object Aware Lidar SLAM Based on Automatic Generation of Training Data,” 2021 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2021) pp. 11641–11647.CrossRefGoogle Scholar
Cheng, J., Sun, Y. and Meng, M. Q.-H., “Robust semantic mapping in challenging environments,” Robotica 38(2), 256–270 (2020).Google Scholar
Chen, G., Wang, B., Wang, X., Deng, H., Wang, B. and Zhang, S., “PSF-LO: Parameterized Semantic Features Based Lidar Odometry,” 2021 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2021) pp. 5056–5062.CrossRefGoogle Scholar
Chen, X., Milioto, A., Palazzolo, E., Giguere, P., Behley, J. and Stachniss, C., “Suma++: Efficient Lidar-based Semantic SLAM,” 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2019) pp. 4530–4537.Google Scholar
Milioto, A., Vizzo, I., Behley, J. and Stachniss, C., “Rangenet++: Fast and Accurate Lidar Semantic Segmentation,” 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2019) pp. 4213–4220.Google Scholar
Chen, Y. and Medioni, G., “Object modelling by registration of multiple range images,” Image Vision Comput. 10(3), 145–155 (1992).Google Scholar
Dewan, A., Caselitz, T., Tipaldi, G. D. and Burgard, W., “Motion-based Detection and Tracking in 3d Lidar Scans,” 2016 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2016) pp. 4508–4513.Google Scholar
Fischler, M. A. and Bolles, R. C., “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24(6), 381–395 (1981).Google Scholar
Dewan, A., Caselitz, T., Tipaldi, G. D. and Burgard, W., “Rigid Scene Flow for 3d Lidar Scans,” 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2016) pp. 1765–1770.Google Scholar
Sualeh, M. and Kim, G.-W., “Semantics aware dynamic slam based on 3d modt,” Ah S. Sens. 21(19), 6355 (2021).CrossRefGoogle ScholarPubMed
Kalman, R. E., A new approach to linear filtering and prediction problems (1960).CrossRefGoogle Scholar
Weng, X., Wang, J., Held, D. and Kitani, K., “3d Multi-Object Tracking: A Baseline and New evaluation Metrics,” 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2020) pp. 10359–10366.CrossRefGoogle Scholar
Shi, S., Wang, X. and Li, H., “Pointrcnn: 3d Object Proposal Generation and Detection from Point Cloud,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2019) pp. 770–779.Google Scholar
Kim, A., Ošep, A. and Leal-Taixé, L., “Eagermot: 3d Multi-Object Tracking via Sensor Fusion,” 2021 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2021) pp. 11315–11321.Google Scholar
Wang, S., Cai, P., Wang, L. and Liu, M., “Ditnet: End-to-end 3d object detection and track id assignment in spatio-temporal world,” IEEE Rob. Autom. Lett. 6(2), 3397–3404 (2021).Google Scholar
Huang, K. and Hao, Q., “Joint Multi-Object Detection and Tracking with Camera-Lidar Fusion for Autonomous Driving,” 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2021) pp. 6983–6989.Google Scholar
Llamazares, Á., Molinos, E. J. and Ocaña, M., “Detection and tracking of moving obstacles (datmo): A review,” Robotica 38(5), 761–774 (2020).Google Scholar
Liu, Z. and Zhang, F., “Balm: Bundle adjustment for lidar mapping,” IEEE Rob. Autom. Lett. 6(2), 3184–3191 (2021).Google Scholar
Triggs, B., McLauchlan, P. F., Hartley, R. I. and Fitzgibbon, A. W., “Bundle Adjustment–a Modern Synthesis,” In: International Workshop On Vision Algorithms (Springer, 1999) pp. 298–372.Google Scholar
Dellenbach, P., Deschaud, J.-E., Jacquet, B. and Goulette, F., “CT-ICP: Real-Time Elastic Lidar Odometry with Loop Closure,” 2022 International Conference on Robotics and Automation (ICRA) (IEEE, 2022) pp. 5580–5586.CrossRefGoogle Scholar
Liu, W., Sun, W. and Liu, Y., “Dloam: Real-time and robust lidar slam system based on CNN in dynamic urban environments,” IEEE Open J. Intell. Transp. Syst. (2021).Google Scholar
Klein, G. and Murray, D., “Parallel Tracking and Mapping for Small AR Workspaces,” 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality (IEEE, 2007) pp. 225–234.Google Scholar
Pire, T., Baravalle, R., D’alessandro, A. and Civera, J., “Real-time dense map fusion for stereo slam,” Robotica 36(10), 1510–1526 (2018).Google Scholar
Forster, C., Pizzoli, M. and Scaramuzza, D., “SVO: Fast Semi-Direct Monocular Visual Odometry,” 2014 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2014) pp. 15–22.Google Scholar
Newcombe, R. A., Lovegrove, S. J. and Davison, A. J., “DTAM: Dense Tracking and Mapping in Real-Time,” 2011 International Conference on Computer Vision (IEEE, 2011) pp. 2320–2327.Google Scholar
Engel, J., Schöps, T. and Cremers, D., “LSD-SLAM: Large-Scale Direct Monocular SLAM,” European Conference on Computer Vision (Springer, 2014) pp. 834–849.Google Scholar
Zhang, J. and Singh, S., “Visual-Lidar Odometry and Mapping: Low-Drift, Robust, and Fast,” 2015 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2015) pp. 2174–2181.CrossRefGoogle Scholar
Shin, Y.-S., Park, Y. S. and Kim, A., “DVL-SLAM: Sparse depth enhanced direct visual-lidar SLAM,” Auton. Rob. 44(2), 115–130 (2020).Google Scholar
Huang, S.-S., Ma, Z.-Y., Mu, T.-J., Fu, H. and Hu, S.-M., “Lidar-Monocular Visual Odometry Using Point and Line Features,” 2020 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2020) pp. 1091–1097.Google Scholar
Engel, J., Koltun, V. and Cremers, D., “Direct sparse odometry,” IEEE Trans. Pattern Anal. 40(3), 611–625 (2017).CrossRefGoogle ScholarPubMed
Shan, T., Englot, B., Meyers, D., Wang, W., Ratti, C. and Rus, D., “LIO-SAM: Tightly-Coupled Lidar Inertial Odometry via Smoothing and Mapping,” 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2020) pp. 5135–5142.Google Scholar
Campos, C., Elvira, R., Rodríguez, J. J. G., Montiel, J. M. and Tardós, J. D., “ORB-SLAM3: An accurate open-source library for visual, visual–inertial, and multimap SLAM,” IEEE Trans. Rob. 37(6), 1874–1890 (2021).Google Scholar
Leutenegger, S., Furgale, P., Rabaud, V., Chli, M., Konolige, K. and Siegwart, R., “Keyframe-based Visual-Inertial SLAM Using Nonlinear Optimization,” Proceedings of Robotis Science and Systems (RSS) 2013 (2013).Google Scholar
Shan, T., Englot, B., Ratti, C. and Rus, D., “LVI-SAM: Tightly-Coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping,” 2021 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2021) pp. 5692–5698.CrossRefGoogle Scholar
Zermas, D., Izzat, I. and Papanikolopoulos, N., “Fast Segmentation of 3d Point Clouds: A Paradigm on Lidar Data for Autonomous Vehicle Applications,” 2017 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2017) pp. 5067–5073.CrossRefGoogle Scholar
Yang, Z., Sun, Y., Liu, S. and Jia, J., “3DSSD: Point-based 3d Single Stage Object Detector,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020) pp. 11040–11048.Google Scholar
Kuhn, H. W., “The hungarian method for the assignment problem,” Nav. Res. Logist. Q. 2(1-2), 83–97 (1955).Google Scholar
Baidu, Apolloauto (2022) https://github.com/ApolloAuto/apollo.Google Scholar
Koide, K., Yokozuka, M., Oishi, S. and Banno, A., “Voxelized GICP for Fast and Accurate 3d Point Cloud Registration,” 2021 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2021) pp. 11054–11059.Google Scholar
Geneva, P., Eckenhoff, K., Yang, Y. and Huang, G., “Lips: Lidar-Inertial 3d Plane SLAM,” 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2018) pp. 123–130.Google Scholar
Hartley, R. and Zisserman, A., Multiple View Geometry in Computer Vision (Cambridge University Press, UK, 2003).Google Scholar
Madsen, K., Nielsen, H. B. and Tingleff, O., Methods for non-linear least squares problems (2004).Google Scholar
Henein, M., Zhang, J., Mahony, R. and Ila, V., “Dynamic SLAM: The Need for Speed,” 2020 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2020) pp. 2123–2129.Google Scholar
Geiger, A., Lenz, P. and Urtasun, R., “Are We Ready for Autonomous Driving? the Kitti Vision Benchmark Suite,” 2012 IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2012) pp. 3354–3361.Google Scholar
Qin, T. and Cao, S., A-loam: A lidar odometry and mapping (2019) https://github.com/HKUST-Aerial-Robotics/A-LOAM.Google Scholar
Wang, H., Wang, C., Chen, C.-L. and Xie, L., “F-loam: Fast Lidar Odometry and Mapping,” 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2021) pp. 4390–4396.Google Scholar
Wang, R., Schworer, M. and Cremers, D., “Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras,” Proceedings of the IEEE International Conference on Computer Vision (2017) pp. 3903–3911.Google Scholar
Wu, H., Han, W., Wen, C., Li, X. and Wang, C., “3d multi-object tracking in point clouds based on prediction confidence-guided data association,” IEEE Trans. Intell. Transp. 23 (6), 5668–5677 (2021).Google Scholar
Sturm, J., Engelhard, N., Endres, F., Burgard, W. and Cremers, D., “A Benchmark for the Evaluation of RGB-D SLAM Systems,” 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE, 2012) pp. 573–580.Google Scholar
Bernardin, K. and Stiefelhagen, R., “Evaluating multiple object tracking performance: The clear mot metrics,” EURASIP J. Image Video Process. 2008(1), 1–10 (2008).Google Scholar
Moore, T. and Stouch, D., “A Generalized Extended Kalman Filter Implementation for the Robot Operating System,” In: Intelligent Autonomous Systems 13 (Springer, 2016) pp. 335–348.CrossRefGoogle Scholar
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