SpringerLink
Log in

Multi-Sensor Fusion Localization

  • Chapter
  • First Online:
Multi-sensor Fusion for Autonomous Driving

  • 438 Accesses

Abstract

In real-time positioning problems, some unstable situations often occur, such as GPS signal loss and map drifting. In order to have better localization and map buildings, this chapter proposes a coarse-and-fine hybrid positioning system, which integrates the global information and feature-based simultaneous localization and map** (GF-SLAM). The system can operate adaptively when external signals are unstable and avoid the cumulative error from local methods. Generally, it applies the feature-based SLAM (F-SLAM) for coarse positioning with particle filter. If available, it fuses the accurate global information by extended Kalman filter (EKF) for precise positioning and revises the deviation in map**, thereby achieving an effective combination of two positioning modes. In addition, we introduce semantic map** and lifelong localization approaches to recognize semi-dynamic objects in non-static environments. We also propose a generic framework that can integrate mainstream object detection algorithms with map** and localization algorithms. The map** method combines an object detection algorithm and a SLAM algorithm to detect semi-dynamic objects and constructs a semantic map that only contains semi-dynamic objects in the environment. In summary, this chapter proposes methods to resolve self-positioning and map drift when positioning fails.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 128.39
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
EUR 171.19
Price includes VAT (Germany)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Aldibaja, M., Suganuma, N., Yoneda, K.: Robust intensity-based localization method for autonomous driving on snow–wet road surface. IEEE Trans. Ind. Inf. 13(5), 2369–2378 (2017)

    Article  Google Scholar 

  2. Andrade-Cetto, J., Sanfeliu, A.: Concurrent map building and localization on indoor dynamic environments. Int. J. Pattern Recognit. Artif. Intell. 16(03), 361–374 (2002)

    Article  Google Scholar 

  3. Aycard, O., Laroche, P., Charpillet, F.: Mobile robot localization in dynamic environments using places recognition. In: Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No. 98CH36146), vol. 4, pp. 3135–3140. IEEE (1998)

    Google Scholar 

  4. Cai, H., Hu, Z., Huang, G., Zhu, D., Su, X.: Integration of gps, monocular vision, and high definition (hd) map for accurate vehicle localization. Sensors 18(10), 3270 (2018)

    Article  Google Scholar 

  5. Chang, L., Niu, X., Liu, T., Tang, J., Qian, C.: Gnss/ins/lidar-slam integrated navigation system based on graph optimization. Remote Sens. 11(9), 1009 (2019)

    Article  Google Scholar 

  6. Chen, S.W., Nardari, G.V., Lee, E.S., Qu, C., Liu, X., Romero, R.A.F., Kumar, V.: Sloam: Semantic lidar odometry and map** for forest inventory. IEEE Robot. Autom. Lett. 5(2), 612–619 (2020)

    Article  Google Scholar 

  7. Chiang, K.W., Tsai, G.J., Chang, H., Joly, C., Ei-Sheimy, N.: Seamless navigation and map** using an ins/gnss/grid-based slam semi-tightly coupled integration scheme. Inf. Fusion 50, 181–196 (2019)

    Article  Google Scholar 

  8. Choi, M.J., Suhr, J.K., Choi, K., Jung, H.G.: Low-cost precise vehicle localization using lane endpoints and road signs for highway situations. IEEE Access 7, 149,846–149,856 (2019)

    Google Scholar 

  9. Fan, Q., Sun, B., Sun, Y., Wu, Y., Zhuang, X.: Data fusion for indoor mobile robot positioning based on tightly coupled ins/uwb. J. Navig. 70(5), 1079–1097 (2017)

    Article  Google Scholar 

  10. Fox, D., Burgard, W., Thrun, S.: Markov localization for mobile robots in dynamic environments. J. Artif. Intell. Res. 11, 391–427 (1999)

    Article  MATH  Google Scholar 

  11. Gao, H., Zhu, J., Zhang, T., **e, G., Kan, Z., Hao, Z., Liu, K.: Situational assessment for intelligent vehicles based on stochastic model and gaussian distributions in typical traffic scenarios. IEEE Trans. Syst. Man Cybern. Syst. 52(3), 1426–1436 (2020)

    Article  Google Scholar 

  12. Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid map** with rao-blackwellized particle filters. IEEE Trans. Robot. 23(1), 34–46 (2007)

    Article  Google Scholar 

  13. Grupp, M.: evo: Python package for the evaluation of odometry and slam (2017). https://github.com/MichaelGrupp/evo

  14. Guivant, J.E., Masson, F.R., Nebot, E.M.: Simultaneous localization and map building using natural features and absolute information. Robot. Auton. Syst. 40(2-3), 79–90 (2002)

    Article  Google Scholar 

  15. Guivant, J.E., Nebot, E.M.: Optimization of the simultaneous localization and map-building algorithm for real-time implementation. IEEE Trans. Robot. Autom. 17(3), 242–257 (2001)

    Article  Google Scholar 

  16. Hahnel, D., Triebel, R., Burgard, W., Thrun, S.: Map building with mobile robots in dynamic environments. In: 2003 IEEE International Conference on Robotics and Automation (Cat. No. 03CH37422), vol. 2, pp. 1557–1563. IEEE (2003)

    Google Scholar 

  17. He, G., Yuan, X., Zhuang, Y., Hu, H.: An integrated gnss/lidar-slam pose estimation framework for large-scale map building in partially gnss-denied environments. IEEE Trans. Instrum. Meas. 70, 1–9 (2020)

    Article  Google Scholar 

  18. Hess, W., Kohler, D., Rapp, H., Andor, D.: Real-time loop closure in 2d lidar slam. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 1271–1278. IEEE (2016)

    Google Scholar 

  19. Hess, W., Kohler, D., Rapp, H., Andor, D.: Real-time loop closure in 2d lidar slam. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 1271–1278. IEEE (2016)

    Google Scholar 

  20. Kohlbrecher, S., Von Stryk, O., Meyer, J., Klingauf, U.: A flexible and scalable slam system with full 3d motion estimation. In: 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics, pp. 155–160. IEEE (2011)

    Google Scholar 

  21. Konolige, K., Grisetti, G., Kümmerle, R., Burgard, W., Limketkai, B., Vincent, R.: Efficient sparse pose adjustment for 2d map**. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 22–29. IEEE (2010)

    Google Scholar 

  22. Li, D., Gao, H.: A hardware platform framework for an intelligent vehicle based on a driving brain. Engineering 4(4), 464–470 (2018)

    Article  Google Scholar 

  23. Linegar, C., Churchill, W., Newman, P.: Work smart, not hard: Recalling relevant experiences for vast-scale but time-constrained localisation. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 90–97. IEEE (2015)

    Google Scholar 

  24. Meyer-Delius, D., Hess, J., Grisetti, G., Burgard, W.: Temporary maps for robust localization in semi-static environments. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5750–5755. IEEE (2010)

    Google Scholar 

  25. Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. Preprint (2018). ar**v:1804.02767

    Google Scholar 

  26. Ren, R., Fu, H., Wu, M.: Large-scale outdoor slam based on 2d lidar. Electronics 8(6), 613 (2019)

    Article  Google Scholar 

  27. Song, Y., Guan, M., Tay, W.P., Law, C.L., Wen, C.: Uwb/lidar fusion for cooperative range-only slam. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 6568–6574. IEEE (2019)

    Google Scholar 

  28. Spangenberg, R., Goehring, D., Rojas, R.: Pole-based localization for autonomous vehicles in urban scenarios. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2161–2166. IEEE (2016)

    Google Scholar 

  29. Tardós, J.D., Neira, J., Newman, P.M., Leonard, J.J.: Robust map** and localization in indoor environments using sonar data. Int. J. Robot. Res. 21(4), 311–330 (2002)

    Article  Google Scholar 

  30. Thrun, S., Burgard, W., Fox, D.: A probabilistic approach to concurrent map** and localization for mobile robots. Auton. Robots 5(3-4), 253–271 (1998)

    Article  MATH  Google Scholar 

  31. Vineet, V., Miksik, O., Lidegaard, M., Nießner, M., Golodetz, S., Prisacariu, V.A., Kähler, O., Murray, D.W., Izadi, S., Pérez, P., et al.: Incremental dense semantic stereo fusion for large-scale semantic scene reconstruction. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 75–82. IEEE (2015)

    Google Scholar 

  32. Wan, G., Yang, X., Cai, R., Li, H., Zhou, Y., Wang, H., Song, S.: Robust and precise vehicle localization based on multi-sensor fusion in diverse city scenes. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 4670–4677. IEEE (2018)

    Google Scholar 

  33. Wang, B.H., Chao, W.L., Wang, Y., Hariharan, B., Weinberger, K.Q., Campbell, M.: Ldls: 3-d object segmentation through label diffusion from 2-d images. IEEE Robot. Autom. Lett. 4(3), 2902–2909 (2019)

    Article  Google Scholar 

  34. Wolf, D.F., Sukhatme, G.S.: Mobile robot simultaneous localization and map** in dynamic environments. Auton. Robots 19(1), 53–65 (2005)

    Article  Google Scholar 

  35. Xu, L., Feng, C., Kamat, V.R., Menassa, C.C.: An occupancy grid map** enhanced visual slam for real-time locating applications in indoor gps-denied environments. Autom. Constr. 104, 230–245 (2019)

    Article  Google Scholar 

  36. Yu, C., Liu, Z., Liu, X.J., **e, F., Yang, Y., Wei, Q., Fei, Q.: Ds-slam: a semantic visual slam towards dynamic environments. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1168–1174. IEEE (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The School of Vehicle and Mobility, Tsinghua University, Bei**g, China

    ** Liu

  2. The School of Vehicle and Mobility, Tsinghua University, Bei**g, China

    Zhenhong Zou

Authors
  1. **nyu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhiwei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hua** Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mo Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Li Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zhenhong Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Zhang, X. et al. (2023). Multi-Sensor Fusion Localization. In: Multi-sensor Fusion for Autonomous Driving. Springer, Singapore. https://doi.org/10.1007/978-981-99-3280-1_6

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-3280-1_6

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-3279-5

  • Online ISBN: 978-981-99-3280-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation