Skip to main content
SpringerLink
Log in

Towards Optimal Dynamic Localization for Autonomous Mobile Robot via Integrating Sensors Fusion

  • Regular Papers
  • Robot and Applications
  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

When it comes to optimal dynamic localization, high accuracy and robustness localization is the main challenge for the autonomous mobile robot. In this paper, an optimal dynamic localization framework with integrating sensors fusion is considered. The global point map is utilized to provide absolute pose observation information, and the multi-sensor information is applied to realize robust localization in complex outdoor environments. The multi-sensor technique, including 3D-Lidar, global positioning system (GPS), and inertial measurement unit (IMU), is adopted to construct the global point map by pose optimization so that the absolute position and attitude observation information can still be provided when the outdoor GPS signal fails. Meanwhile, in the case of optimal localization, the system kinematics equation is constructed by the IMU error model, and the map pose is matched by map scanning. Moreover, the GPS position information participates in multi-source fusion when the GPS signal is reliable. Finally, the experimental results show that the average localization error is within 0.05 meters, reflecting the flexibility of dynamic localization.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. K.-S. Hong and P.-T. Pham, “Control of axially moving systems: A review,” International Journal of Control, Automation, and Systems, vol. 17, no. 12, pp. 2983–3008, 2019.

    Article  Google Scholar 

  2. S. Muhammad and G.-W. Kim, “Simultaneous localization and mapping in the epoch of semantics: A survey,” International Journal of Control, Automation, and Systems, vol. 17, no. 3, pp. 729–742, 2019.

    Article  Google Scholar 

  3. J. Li, J. Wang, S. Wang, W. Qi, L. Zhang, Y. Hu, and H. Su, “Neural approximation-based model predictive tracking control of non-holonomic wheel-legged robots,” International Journal of Control, Automation, and Systems, vol. 19, no. 1, pp. 372–381, 2021.

    Article  Google Scholar 

  4. J. Li, J. Wang, S. Wang, and C. Yang, “Human-robot skill transmission for mobile robot via learning by demonstration,” Neural Computing and Applications, pp. 1–11, 2021. DOI: https://doi.org/10.1007/s00521-021-06449-x

  5. Y. Dai, J. Wang, J. Li, and J. Li, “MDRNet: A lightweight network for real-time semantic segmentation in street scenes,” Assembly Automation, vol. 41, no. 6, pp. 725–733, 2021.

    Article  Google Scholar 

  6. J. Li, J. Wang, H. Peng, L. Zhang, Y. Hu, and H. Su, “Neural fuzzy approximation enhanced autonomous tracking control of the wheel-legged robot under uncertain physical interaction,” Neurocomputing, vol. 410, pp. 342–353, 2020.

    Article  Google Scholar 

  7. J. Li, Y. Dai, J. Wang, X. Su, and R. Ma, “Towards broad learning networks on unmanned mobile robot for semantic segmentation,” Proc. of IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 9228–9234, 2022.

  8. N. Kawabata, Y. Kuwabara, and T. Kawasaki, “Self-localization of autonomous car using autoware,” IEICE Technical Report, vol. 120, no. 389, pp. 103–108, 2021.

    Google Scholar 

  9. J. Li, H. Qin, J. Wang, and J. Li, “OpenStreetMap-based autonomous navigation for the four wheel-legged robot via 3d-lidar and CCD camera,” IEEE Transactions on Industrial Electronics, vol. 69, no. 3, pp. 2708–2717, 2022.

    Article  Google Scholar 

  10. S. Kuutti, S. Fallah, K. Katsaros, M. Dianati, F. Mccullough, and A. Mouzakitis, “A survey of the state-of-the-art localization techniques and their potentials for autonomous vehicle applications,” IEEE Internet of Things Journal, vol. 5, no. 2, pp. 829–846, 2018.

    Article  Google Scholar 

  11. J. Li, X. Zhang, J. Li, Y. Liu, and J. Wang, “Building and optimization of 3D semantic map based on Lidar and camera fusion,” Neurocomputing, vol. 409, pp. 394–407, 2020.

    Article  Google Scholar 

  12. E. Yurtsever, J. Lambert, A. Carballo, and K. Takeda, “A survey of autonomous driving: Common practices and emerging technologies,” IEEE Access, vol. 8, pp. 58443–58469, 2020.

    Article  Google Scholar 

  13. Y. Dai, J. Li, J. Wang, and J. Li, “Towards extreme learning machine framework for lane detection on unmanned mobile robot,” Assembly Automation, vol. 42, no. 3, pp. 361–371, 2022.

    Article  Google Scholar 

  14. E. Stenborg, Long-term Localization for Self-driving Cars, Ph.D. dissertation, Chalmers Univeristy of Technology, 2020.

  15. J. Liu and G. Guo, “Vehicle localization during gps outages with extended Kalman filter and deep learning,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–10, 2021.

    Article  Google Scholar 

  16. J. Liu and G. Guo, “Vehicle localization during gps outages with extended kalman filter and deep learning,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–10, 2021.

    Article  Google Scholar 

  17. R. Mur-Artal, J. M. M. Montiel, and J. D. Tardos, “ORS-SLAM: A versatile and accurate monocular SLAM system,” IEEE Transactions on Robotics, vol. 31, no. 5, pp. 1147–1163, 2015.

    Article  Google Scholar 

  18. J. Zhang and S. Singh, “Low-drift and real-time lidar odometry and mapping,” Autonomous Robots, vol. 41, no. 2, pp. 401–416, 2017.

    Article  Google Scholar 

  19. A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” Proc. of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp. 3354–3361, 2012.

  20. A. Ranganathan, D. Ilstrup, and T. Wu, “Light-weight localization for vehicles using road markings,” Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, pp. 921–927, 2013.

  21. X. Li, S. Du, G. Li, and H. Li, “Integrate point-cloud segmentation with 3D lidar scan-matching for mobile robot localization and mapping,” Sensors, vol. 20, no. 1, p. 237, 2020.

    Article  MathSciNet  Google Scholar 

  22. J. K. Suhr, J. Jang, D. Min, and H. G. Jung, “Sensor fusion-based low-cost vehicle localization system for complex urban environments,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 5, pp. 1078–1086, 2016.

    Article  Google Scholar 

  23. X. Lin, F. Wang, B. Yang, and W. Zhang, “Autonomous vehicle localization with prior visual point cloud map constraints in gnss-challenged environments,” Remote Sensing, vol. 13, no. 3, p. 506, 2021.

    Article  Google Scholar 

  24. R. W. Wolcott and R. M. Eustice, “Fast lidar localization using multiresolution gaussian mixture maps,” Proc. of IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 2814–2821, 2015.

  25. N. Akai, L. Y. Morales, E. Takeuchi, Y. Yoshihara, and Y. Ninomiya, “Robust localization using 3D NDT scan matching with experimentally determined uncertainty and road marker matching,” Proc. of IEEE Intelligent Vehicles Symposium (IV), IEEE, pp. 1356–1363, 2017.

  26. R. E. Kalman, “A new approach to linear filtering and prediction problems,” Journal of Basic Engineering, vol. 82, no. 1, pp. 35–45, 1960. 1960.

    Article  MathSciNet  Google Scholar 

  27. M. Mehdikhani, Integration of a Low-cost Gyro in the Localization of an Industrial Mobile Robot via an Rrror-state Extended Kalman Filter, Master’s thesis, ING, 2021.

  28. T. Shan and B. Englot, “LeGO-LOAM: Lightweight and ground-optimized lidar odometry and mapping on variable terrain,” Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 4758–4765, 2018.

  29. T. Shan, B. Englot, D. Meyers, W. Wang, C. Ratti, and D. Rus, “LIO-SAM: Tightly-coupled lidar inertial odometry via smoothing and mapping,” Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 5135–5142, 2020.

  30. C. Qin, H. Ye, C. E. Pranata, J. Han, S. Zhang, and M. Liu, “LINS: A lidar-inertial state estimator for robust and efficient navigation,” Proc. of IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 8899–8906, 2020.

  31. J. Sola, “Quaternion kinematics for the error-state kalman filter,” arXiv preprint arXiv:1711.02508, 2017.

  32. G. Kim and A. Kim, “Scan context: Egocentric spatial descriptor for place recognition within 3D point cloud map,” Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 4802–4809, 2018.

  33. B. Sheng, S. Wenzhong, F. Wenzheng, C. Pengxin, N. Mingyan, and X. Haodong, “A tight coupling mapping method to integrate the ESKF, g2o, and point cloud alignment,” The Journal of Supercomputing, vol. 78, pp. 1903–1922, 2022.

    Article  Google Scholar 

  34. H. Lim, S. Hwang, S. Shin, and H. Myung, “Normal distributions transform is enough: Real-time 3D scan matching for pose correction of mobile robot under large odometry uncertainties,” Proc. of 20th International Conference on Control, Automation and Systems (ICCAS), IEEE, pp. 1155–1161, 2020.

  35. S. Srinara, C.-M. Lee, S. Tsai, G.-J. Tsai, and K.-W. Chiang, “Performance analysis of 3D BDT scan matching for autonomous vehicles using INS/GNSS/3D LiDAR-SLAM integration scheme,” Proc. of IEEE International Symposium on Inertial Sensors and Systems (INERTIAL), pp. 1–4, 2021.

  36. J. Li, R. Li, J. Li, J. Wang, Q. Wu, and X. Liu, “Dualview 3D object recognition and detection via Lidar point cloud and camera image,” Robotics and Autonomous Systems, vol. 150, 103999, 2022.

    Article  Google Scholar 

  37. S. Wang, Z. Chen, J. Li, J. Wang, J. Li, and J. Zhao, “Flexible motion framework of the six wheel-legged robot: Experimental results,” IEEE/ASME Transactions on Mechatronics, vol. 27, no. 4, pp. 2246–2257, 2022.

    Article  Google Scholar 

  38. J. Li, J. Wang, H. Peng, Y. Hu, and H. Su, “Fuzzy-torque approximation-enhanced sliding mode control for lateral stability of mobile robot,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 4, pp. 2491–2500, 2022.

    Article  Google Scholar 

  39. Z. Chen, J. Li, S. Wang, J. Wang, and L. Ma, “Flexible gait transition for six wheel-legged robot with unstructured terrains,” Robotics and Autonomous Systems, vol. 150, 103989, 2022.

    Article  Google Scholar 

  40. J. Li, Y. Dai, X. Su, and W. Wu, “Efficient dual-branch bottleneck networks of semantic segmentation based on CCD camera,” Remote Sensing, vol. 14, no. 16, p. 3925, 2022.

    Article  Google Scholar 

  41. K. Zheng, “ROS navigation tuning guide,” Robot Operating System (ROS), pp. 197–226, Springer, 2021.

  42. Y. Zhu, B. Xue, L. Zheng, H. Huang, M. Liu, and R. Fan, “Real-time, environmentally-robust 3d lidar localization,” Proc. of IEEE International Conference on Imaging Systems and Techniques (IST), pp. 1–6, 2019.

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China

    Jing Li, Keyan Guo & Junzheng Wang

  2. Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, College of Engineering, South China Agricultural University, Guangzhou, 510642, China

    Jiehao Li

  3. State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing, 100081, China

    Jiehao Li

Authors
  1. Jing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Keyan Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Junzheng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiehao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jing Li or Jiehao Li.

Ethics declarations

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was supported by the National Key Research and Development Program of China under Grant 2019YFC1511401, and the National Natural Science Foundation of China under Grant 62173038 and 62203176.

Jing Li was born in 1982. She received her M.S. degree of engineering from Shandong University of Technology in 2007, and a Ph.D. degree in control science and engineering from Beijing Institute of Technology in 2011. She is now an associate professor of School of Automation, State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology. Her research interests include image detection technology, and object detection and tracking.

Keyan Guo was born in 1997. He received his B.S. degree in automation from Ocean University of China, Qingdao, China, in 2019. He is currently pursuing an M.S. degree as a member of State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, China. His current research interests include simultaneous localization and mapping of mobile robot.

Junzheng Wang received his Ph.D. degree in control science and engineering from the Beijing Institute of Technology, Beijing, China, in 1994. He is the Deputy Director with the State Key Laboratory of Intelligent Control and Decision of Complex Systems, the Director of the Key Laboratory of Servo Motion System Drive and Control, and the Dean of the Graduate School of Beijing Institute of Technology, where he is a Professor and a Ph.D. Supervisor. His current research interests include motion control, electric hydraulic servo system, and dynamic target detection and tracking based on image technology. He received the Second Award from the National Scientific and Technological Progress (No.1) of China.

Jiehao Li received his M.Sc. degree in control engineering at South China University of Technology, Guangzhou, China, in 2017. He received a Ph.D. degree at the State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, China, in 2022. He is now an Associate Professor at College of Engineering, South China Agricultural University, Guangzhou, China. He is also a Visiting Fellow of the Medical and Robotic Surgery Group (NEARLab) in Politecnico di Milano, Milano, Italy. His research interests mainly include mobile robotics, motion control, robot vision, and image processing. Prof. Li is the Academic Committee Member of Youth Working Committee of CAAI and CICC. He has been awarded the Best Conference Paper Finalist of IEEE ICARM2020, the Outstanding Reviewer of CAC2021, and the Outstanding Session Chair of WRC SARA2022. He is the Conference Session Chair of IEEE ICUS2022, ICIRA2022 and YAC2022. He has served as the Guest Editor of IET Control Theory & Applications, Frontiers in Neurorobotics, and the Associate Editor of Journal of Control Science and Engineering.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, J., Guo, K., Wang, J. et al. Towards Optimal Dynamic Localization for Autonomous Mobile Robot via Integrating Sensors Fusion. Int. J. Control Autom. Syst. 21, 2648–2663 (2023). https://doi.org/10.1007/s12555-021-1088-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-021-1088-7

Keywords

Search

Navigation