High-precision and robust localization in GNSS-denied areas is crucial for autonomous vehicles and robots. Most state-of-the-art localization methods are based on simultaneous localization and mapping (SLAM) with a camera or light detection and ranging (LiDAR). However, SLAM will suffer from drift during long-term running without loop closure or prior constraints. Lightweight, high-precision environmental maps have gradually become an indispensable part of future autonomous driving. In order to solve the problem of real-time global localization for autonomous vehicles and robots, we propose a precise and robust LiDAR localization system based on a pre-built, occupied high-definition (HD) map called the Extended QuadTree (EQT) map. It makes use of a planar quadtree for block division and a Z-sequence index structure within the block cells. Then, a four-level occupancy probability cell value model is adopted. It will save about eight times the storage space compared with Google Cartographer, and the EQT map can be extended to store other information. For efficient scan-to-map matching with our specialized EQT map, the Bursa linearized model is used in the Gauss–Newton iteration of our algorithm, which makes the calculation of partial derivatives fast. All the above improvements lead to optimal storage and efficient querying for real-time scan-to-map matching localization. Field tests in an industrial park and road environment prove that positioning accuracy of about 6–13 cm and attitude accuracy of about 0.15° were achieved using a VLP-16 LiDAR. They also show that the method proposed in this paper is significantly better than the NDT method. For the long and narrow environment of an underground mine tunnel, high-resolution maps are also helpful for accurate and robust localization.
Extrinsic Calibration between LiDAR and POS (Position and Orientation System) is a fundamental prerequisite for varieties of MLS (Mobile Laser Scanner) applications. Due to the sparse structure of LiDAR data, the current calibration methods relying on common point feature matching are unreliable, and the low accuracy POS results make the extrinsic calibration of MLS system more challenging. In this paper, we propose an incremental estimation method of six degree of freedom extrinsic transformation of LiDAR and POS.
Firstly, the POS-SLAM is used to accumulate LiDAR scans as online sub maps. Attitudes of the carrier are calculated by using GNSS/INS loose combination method of bidirectional adjustment, and scans are associated with sub map based on the time interpolation. Then, the extrinsic calibration parameters are estimated by optimizing corresponding points difference between SLAM and MLS coordinate frameframe. Finally, field tests have been conducted to the proposed method. RMS between the map by the calibrated MLS and by the static measurement is 0.57cm. The results demonstrate that the accuracy and robustness of our calibration approach are sufficient for mapping requirement of MLS
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