Coarse-to-Fine Loosely-Coupled LiDAR-Inertial Odometry for Urban Positioning and Mapping

Abstract

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.

A Coarse-to-Fine LiDAR-Based SLAM with Dynamic Object Removal in Dense Urban Areas

“…Moreover, we will study to reweight the dynamic points for state estimation rather than simply remove the object that might lead to the artificial edge [20,33] in the SLAM system. The lidar-inertial odometry [34] is another interesting topic to reduce positioning error. Last but not least, we will evaluate the performance under diverse urban areas.…”

Continuous GNSS-RTK Aided by LiDAR/Inertial Odometry with Intelligent GNSS Selection in Urban Canyons

“…There are three kinds of inputs, LiDAR range measurements, IMU ego-motion measurements and GNSS-RTK position measurements. Firstly, the LiDAR/inertial odometry implemented as our previous work LC-LIO [6] generates a locally accurate 6D motion estimation and a registered point cloud map continuously. In LC-LIO [6], LiDAR and IMU measurement modeling is accomplished via feature-based scan-to-map registration [4] and preintegration [10] respectively.…”

“…Firstly, the LiDAR/inertial odometry implemented as our previous work LC-LIO [6] generates a locally accurate 6D motion estimation and a registered point cloud map continuously. In LC-LIO [6], LiDAR and IMU measurement modeling is accomplished via feature-based scan-to-map registration [4] and preintegration [10] respectively. The two modeling procedures proceed separately.…”

“…Besides, it will correct the bias of the accelerometer and gyroscope for accurate pre-integration. For more details our previously published work [6] are recommended. Secondly, with the assistance of the registered 3D point cloud map, the available GNSS-RTK Selection module discard unavailable GNSS RTK and preserve available GNSS-RTK position measurements, which are more likely to be in deep urban canyons and light urban environments respectively.…”

“…However, our previous work in [5] showed that the performance of LiDAR odometry can be significantly challenged in urban canyons due to the numerous unexpected dynamic objects. To alleviate this problem, our recent work in [6] proposed a coarse-to-fine LiDAR/inertial odometry by combining the IMU and 3D LiDAR which led to robust and accurate odometry. Undoubtedly, the combination of the GNSS-RTK, IMU, and 3D LiDAR is a promising solution to provide locally accurate (take advantage of LiDAR inertial odometry) and free-of-drift absolute positioning (take advantage of GNSS-RTK).…”