Abstract
Continuous and reliable positioning anywhere anytime is one of the critical technologies for autonomous driving, whereas it is challenging yet, especially in urban environments. Due to the presence of buildings, Global Navigation Satellite System (GNSS) is not reliable for degraded positioning accuracy and availability. With the simultaneous localization and mapping (SLAM) approach, Light detection and ranging (LiDAR) has been widely used by autonomous driving vehicles for localization and environmental perception. However, LiDAR odometry (LO) or SLAM is not accurate enough for autonomous driving as it suffers from accumulative errors. This study proposes a GNSS/LiDAR/Map integrated positioning solution to provide ubiquitous positioning for autonomous driving, especially in urban areas, including urban canyons and underground, where GNSS is blocked partly or even fully. Matching LiDAR scans with a 3D map, named 3D map-based global localization (MGL), can provide an absolute positioning solution with respect to the map. A drift error model of LiDAR odometry is proposed to improve the accuracy of LiDAR odometry, and its model parameters are estimated online using either GNSS or MGL absolute locations. Using the graph optimization approach, GNSS and/or MGL precise absolute positioning are integrated with LO relative positioning to provide continuous and reliable positioning even when GNSS is degraded. The proposed solution is validated using the NCLT public dataset, which contains LiDAR data for building 3D maps and 3D map-based global localization, respectively. Based on the NCLT dataset, we simulate three typical urban scenarios to verify the performance of ubiquitous positioning. The experiments show that the proposed drift error correction method decreases accumulated error of LiDAR odometry by 35.5%, and the proposed GNSS/LiDAR/Map solution can provide continuous positioning with the availability of 100%, and improves the positioning accuracy by 49.8%, compared to the state-of-the-art approach of GNSS/LO fusion.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
The NCLT datasets used in the current study are available from http://robots.engin.umich.edu/nclt/.
References
Bhamidipati S, Gao GX (2020) Integrity monitoring of graph-slam using gps and fish-eye camera. Navigation 67(3):583–600. https://doi.org/10.1002/navi.381
Bosse M, Zlot R (2013) Place recognition using keypoint voting in large 3d lidar datasets. In: IEEE International Conference on Robotics & Automation
Carlevaris-Bianco N, Ushani AK, Eustice RM (2016) University of michigan north campus long-term vision and lidar dataset. Int J Robotics Res 35(9):1023–1035. https://doi.org/10.1177/0278364915614638
Chang L, Niu X, Liu T et al (2019) Gnss/ins/lidar-slam integrated navigation system based on graph optimization. Remote Sensing 11(9):1009. https://doi.org/10.3390/rs11091009
Chen S (2020) Research on slam based on lidar/visual fusion (lv-slam). doctor, https://doi.org/10.27379/d.cnki.gwhdu.2020.000697
Elbaz G, Avraham T, Fischer A (2017) 3d point cloud registration for localization using a deep neural network auto-encoder. 30th Ieee Conference on Computer Vision and Pattern Recognition (Cvpr 2017) pp 2472–2481. https://doi.org/10.1109/Cvpr.2017.265, \(<\)GotoISI\(>\)://WOS:000418371402057 https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp= &arnumber=8099748 &ref=
Finman R, Paull L, Leonard JJ (2015) Toward object-based place recognition in dense rgb-d maps. In: ICRA Workshop Visual Place Recognition in Changing Environments, Seattle, WA
Gao Y, Liu S, Atia MM, et al (2015) Ins/gps/lidar integrated navigation system for urban and indoor environments using hybrid scan matching algorithm. Sensors (Basel) 15(9):23,286–302. https://doi.org/10.3390/s150923286, https://www.ncbi.nlm.nih.gov/pubmed/26389906
Grisetti G, Stachniss C, Burgard W (2007) Improved techniques for grid mapping with rao-blackwellized particle filters. Ieee Transactions on Robotics 23(1):34–46. https://doi.org/10.1109/Tro.2006.889486\(<\)GotoISI\(>\)://WOS:000244311500004
Grisetti G, Kummerle R, Stachniss C et al (2010) A tutorial on graph-based slam. IEEE Intell Transp Syst Magazine 2(4):31–43. https://doi.org/10.1109/mits.2010.939925
He G, Yuan X, Zhuang Y et al (2021) An integrated gnss/lidar-slam pose estimation framework for large-scale map building in partially gnss-denied environments. IEEE Trans Instrum Measure 70:1–9. https://doi.org/10.1109/tim.2020.3024405
Hess W, Kohler D, Rapp H, et al (2016) Real-time loop closure in 2d lidar slam. In: 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp 1271–1278
Holz D, Ichim A, Tombari F et al (2015) Registration with the point cloud library - a modular framework for aligning in 3-d. IEEE Robotics Autom Mag 22(4):110–124
Ji Z, Singh S (2014) Loam: Lidar odometry and mapping in real-time. In: Robotics: Science and Systems Conference
Joerger M, Spenko M (2017) Towards navigation safety for autonomous cars. Inside GNSS
Julier SJ, Durrant-Whyte HF (2003) On the role of process models in autonomous land vehicle navigation systems. IEEE Trans Robot Autom 19:1–14. https://doi.org/10.1109/TRA.2002.805661
Kim J, Sukkarieh S (2005) 6dof slam aided gnss/ins navigation in gnss denied and unknown environments. J Global Position Syst 4(1 &2):120–128. https://doi.org/10.5081/jgps.4.1.120
Kohlbrecher S, Stryk OV, Meyer J, et al (2011) A flexible and scalable slam system with full 3d motion estimation. In: IEEE International Symposium on Safety
Kukko A, Kaijaluoto R, Kaartinen H et al (2017) Graph slam correction for single scanner mls forest data under boreal forest canopy. ISPRS J Photogramm Remote Sensing 132:199–209. https://doi.org/10.1016/j.isprsjprs.2017.09.006
Li P, Wang R, Wang Y et al (2020) Evaluation of the icp algorithm in 3d point cloud registration. IEEE Access 8:68. https://doi.org/10.1109/access.2020.2986470
Li W, Cui X, Lu M (2020b) Feature-based tightly-integrated rtk/ins/lidar fusion positioning algorithm in ambiguity domain. In: China Satellite Navigation Conference, Springer, pp 510–526
Li Y, Zahran S, Zhuang Y et al (2019) Imu/magnetometer/barometer/mass-flow sensor integrated indoor quadrotor uav localization with robust velocity updates. Remote Sensing 11(7):838
Lu W, Wan G, Zhou Y, et al (2019) Deepicp: An end-to-end deep neural network for 3d point cloud registration. arxiv 2019. http://arxiv.org/abs/1905.04153
Lu W, Zhou Y, Wan G, et al (2020) L3-net: Towards learning based lidar localization for autonomous driving. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Ma H, Yin DY, Liu JB et al (2022) 3d convolutional auto-encoder based multi-scale feature extraction for point cloud registration. Optics & Laser Technol 149:78. https://doi.org/10.1016/j.optlastec.2022.107860
Magnusson M (2009) The three-dimensional normal-distributions transform:an efficient representation for registration, surface analysis, and loop detection. PhD thesis, orebro university
Novatel (2022) https://novatel.com/
Pierzchała M, Giguère P, Astrup R (2018) Mapping forests using an unmanned ground vehicle with 3d lidar and graph-slam. Computers Electron Agric 145:217–225. https://doi.org/10.1016/j.compag.2017.12.034
Qian C, Zhang H, Li W et al (2020) A lidar aiding ambiguity resolution method using fuzzy one-to-many feature matching. J Geodesy 94:10. https://doi.org/10.1007/s00190-020-01426-z
Rusu RB, Blodow N, Beetz M (2009) Fast point feature histograms (fpfh) for 3d registration. Icra: 2009 Ieee International Conference on Robotics and Automation, Vols 1-7 pp 1848–1853. \(<\)GotoISI\(>\)://WOS:000276080400298
Schultz A, Gilabert R, Bharadwaj A, et al (2016) A navigation and mapping method for uas during under-the-canopy forest operations. In: 2016 IEEE/ION Position, Location and Navigation Symposium - PLANS 2016
Shamsudin AU, Ohno K, Hamada R et al (2018) Consistent map building in petrochemical complexes for firefighter robots using slam based on gps and lidar. ROBOMECH J 5:1. https://doi.org/10.1186/s40648-018-0104-z
Shan T, Englot B (2019) Lego-loam: Lightweight and ground-optimized lidar odometry and mapping on variable terrain. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Shan T, Englot B, Meyers D, et al (2020) Lio-sam: Tightly-coupled lidar inertial odometry via smoothing and mapping. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp 5135–5142
Steder B, Grisetti G, Burgard W (2010) Robust place recognition for 3d range data based on point features. 2010 Ieee International Conference on Robotics and Automation (Icra) pp 1400–1405. https://doi.org/10.1109/Robot.2010.5509401, \(<\)GotoISI\(>\)://WOS:000284150001132 https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp= &arnumber=5509401 &ref=
Stephenson S, Meng X, Moore T, et al (2011) Accuracy requirements and benchmarking position solutions for intelligent transportation location based services. In: Proceedings of the 8th international symposium on location-based services
Wang H, Wang C, Chen CL, et al (2021) F-loam : Fast lidar odometry and mapping. 2021 Ieee/Rsj International Conference on Intelligent Robots and Systems (Iros) pp 4390–4396. https://doi.org/10.1109/Iros51168.2021.9636655, \(<\)GotoISI\(>\)://WOS:000755125503067 https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp= &arnumber=9636655 &ref=
Wang ZM, Zhang Q, Li JS, et al (2019) A computationally efficient semantic slam solution for dynamic scenes. Remote Sensing 11(11). https://doi.org/10.3390/rs11111363, https://mdpi-res.com/d_attachment/remotesensing/remotesensing-11-01363/article_deploy/remotesensing-11-01363.pdf?version=1559823029
West KF, Webb BN, Lersch JR, et al (2004) Context-driven automated target detection in 3D data. In: Sadjadi FA (ed) Automatic Target Recognition XIV, International Society for Optics and Photonics, vol 5426. SPIE, pp 133 – 143, https://doi.org/10.1117/12.542536
Xu D, Liu J, Hyyppä J et al (2022) A heterogeneous 3d map-based place recognition solution using virtual lidar and a polar grid height coding image descriptor. ISPRS J Photogram Remote Sensing 183:1–18. https://doi.org/10.1016/j.isprsjprs.2021.10.020
Xu D, Liu J, Liang Y et al (2022) A lidar-based single-shot global localization solution using a cross-section shape context descriptor. ISPRS J Photogramm Remote Sensing 189:272–288. https://doi.org/10.1016/j.isprsjprs.2022.05.005
Yurtsever E, Lambert J, Carballo A et al (2020) A survey of autonomous driving: Common practices and emerging technologies. IEEE Access 8:58443–58469
Zeng A, Song SR, Niessner M, et al (2017) 3dmatch: Learning local geometric descriptors from rgb-d reconstructions. 30th Ieee Conference on Computer Vision and Pattern Recognition (Cvpr 2017) pp 199–208. https://doi.org/10.1109/Cvpr.2017.29, \(<\)GotoISI\(>\)://WOS:000418371400022 https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp= &arnumber=8099512 &ref=
Zhang J, Singh S (2017) Low-drift and real-time lidar odometry and mapping. Autonomous Robots 41(2):401–416
Acknowledgements
This study was supported in part by National Key Research Development Program of China with project No. 2021YFB2501102, the Natural Science Fund of China with Project No. 41874031 and 42111530064, Shenzhen Science and Technology Program with Project No. JCYJ20210324123611032, Wuhan Knowledge Innovation Research Program with Project No. 2022010801010109.
Author information
Authors and Affiliations
Contributions
Jingbin Liu designed this research; Jingbin Liu and Yifan Liang designed the experiments; Yifan Liang, Dong Xu and Xiaodong Gong wrote the program code and performed the experiments; Jingbin Liu and Yifan Liang wrote the paper; Juha Hyyppä edited the manuscript.
Corresponding author
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Liu, J., Liang, Y., Xu, D. et al. A ubiquitous positioning solution of integrating GNSS with LiDAR odometry and 3D map for autonomous driving in urban environments. J Geod 97, 39 (2023). https://doi.org/10.1007/s00190-023-01728-y
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s00190-023-01728-y