Accurate positioning and mapping are significant for autonomous systems with navigation requirements. In this paper, a coarse-to-fine loosely-coupled (LC) LiDAR-inertial odometry (LC-LIO) that could explore the complementariness of LiDAR and inertial measurement unit (IMU) was proposed for the real-time and accurate pose estimation of a ground vehicle in urban environments. Different from the existing tightly-coupled (TC) LiDAR-inertial fusion schemes which directly use all the considered ranges and inertial measurements to optimize the vehicle pose, the method proposed in this paper performs loosely-couped integrated optimization with the high-frequency motion prediction, which was produced by IMU integration based on optimized results, employed as the initial guess of LiDAR odometry to approach the optimality of LiDAR scan-to-map registration. As one of the prominent contributions, thorough studies were conducted on the performance upper bound of the TC LiDAR-inertial fusion schemes and LC ones, respectively. Furthermore, the experimental verification was performed on the proposition that the proposed pipeline can fully relax the potential of the LiDAR measurements (centimeter-level ranging accuracy) in a coarse-to-fine way without being disturbed by the unexpected IMU bias. Moreover, an adaptive covariance estimation method employed during LC optimization was proposed to explain the uncertainty of LiDAR scan-to-map registration in dynamic scenarios. Furthermore, the effectiveness of the proposed system was validated on challenging real-world datasets. Meanwhile, the process that the proposed pipelines realized the coarse-to-fine LiDAR scan-to-map registration was presented in detail. Comparing with the existing state-of-the-art TC-LIO, the focus of this study would be placed on that the proposed LC-LIO work could achieve similar or better accuracy with a reduced computational expense.

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Global Navigation Satellite System Real-time Kinematic (GNSS-RTK) is an indispensable source for the absolute positioning of autonomous systems. Unfortunately, the performance of the GNSS-RTK is significantly degraded in urban canyons, due to the notorious multipath and Non-Line-of-Sight (NLOS). On the contrary, LiDAR/inertial odometry (LIO) can provide locally accurate pose estimation in structured urban scenarios but is subjected to drift over time. Considering their complementarities, GNSS-RTK, adaptively integrated with LIO was proposed in this paper, aiming to realize continuous and accurate global positioning for autonomous systems in urban scenarios. As one of the main contributions, this paper proposes to identify the quality of the GNSS-RTK solution based on the point cloud map incrementally generated by LIO. A smaller mean elevation angle mask of the surrounding point cloud indicates a relatively open area thus the correspondent GNSS-RTK would be reliable. Global factor graph optimization is performed to fuse reliable GNSS-RTK and LIO. Evaluations are performed on datasets collected in typical urban canyons of Hong Kong. With the help of the proposed GNSS-RTK selection strategy, the performance of the GNSS-RTK/LIO integration was significantly improved with the absolute translation error reduced by more than 50%, compared with the conventional integration method where all the GNSS-RTK solutions are used.

A low‐cost and accurate positioning solution is significant for the massive deployment of fully autonomous driving vehicles (ADV). Conventional mechanical LiDAR has proven its performance, but its high cost hinders the massive production of autonomous vehicles. This paper proposes a low‐cost LiDAR/inertial‐based localization solution for autonomous systems with prior maps in urban areas. Instead of relying on the costly mechanical LiDAR, this paper proposes to utilize the solid‐state LiDAR (SSL) with the prior map to estimate the position of the vehicle by matching the real‐time point clouds from the SSL and the prior map using the normal distribution transformation (NDT) algorithm. However, the field of view (FOV) of the SSL is signifcantly smaller than the conventional mechanical LiDAR, which can easily lead to failure during the NDT map matching. To fill this gap, this paper proposes to exploit the complementariness of the inertial measurement unit (IMU) and the SSL, where the IMU pre‐integration provides a coarse but high‐frequency initial guess to the map matching. To evaluate the effectiveness of the proposed method in this paper, the authors collect the dataset in two typical urban scenarios through a pedestrian hand‐hold and a vehicle driving condition. The results reveal that the SSL‐only‐based localization is significantly challenged in dynamic scenarios. With the help of the IMU, the robustness of the proposed method is significantly improved, achieving an accuracy of within 0.5 m. To show the sensitivity of the SSL‐based map matching against the initial guess of the state, this paper also presents the convergence results of the map matching under different levels of accuracy in terms of the initial guess.

His research interests including localization and sensor fusion for autonomous driving.Shen Donghui received his bachelor's degree from Shandong University in Space Science and Technology in 2017 and MSc in Space Physics at Shandong University in 2020. His research interests including 3D perception and HD Map updating for autonomous driving.Weisong Wen was born in Ganzhou, Jiangxi, China. He received a Ph.D. degree in mechanical engineering, the Hong Kong Polytechnic University, in 2020. He is currently a senior research fellow at the Hong Kong Polytechnic University. His research interests include multi-sensor integrated localization for autonomous vehicles, SLAM, and GNSS positioning in urban canyons. He was a visiting student researcher at the University of California, Berkeley (UCB) in 2018.ZHANG Jiachen received her bachelor's degree from Tianjin University in Information Engineering in 2016 and is currently an enrolled, full-time graduate student at Tianjin University, majoring in Optical Engineering. She is working as a research assistant in the Intelligent Positioning and Navigation Laboratory. Her research interests including localization and sensor fusion for autonomous driving.Li-Ta Hsu received the B.S. and Ph.D. degrees in aeronautics and astronautics from National Cheng Kung University, Taiwan, in 2007 and 2013, respectively. He is currently an assistant professor with the Division of Aeronautical and Aviation Engineering, Hong Kong Polytechnic University, before he served as post-doctoral researcher in Institute of Industrial Science at University of Tokyo, Japan. In 2012, he was a visiting scholar in University College London, U.K. He was a technical representative in ION in 2019-2021 and is an Associate Fellow of RIN. His research interests include GNSS positioning in challenging environments and localization for pedestrian, autonomous driving vehicle and unmanned aerial vehicle.