PDLC-LIO: A Precise and Direct SLAM System Toward Large-Scale Environments With Loop Closures

As a key technology of autonomous driving, the high-precision vehicle localization is the prerequisite for automatic steer. With the advantage of accuracy and robustness, the Light Detection and Ranging (LiDAR) is widely employed in Simultaneous Localization and Mapping (SLAM), which has been utilized to provide reliable positioning information. Compared with the experimental vehicles in the restricted laboratory driving circumstances, autonomous vehicles are devoted to actual roads and contain complicated activities, where closed loops can be noticed. In general, the accumulated error may be accumulated with the increasing of maneuver scope of SLAM. A distinct drift phenomenon may thus be incurred in the completed point cloud map and threaten the accuracy of positioning results. Focusing on this deficiency, a tightly-coupled LiDAR inertial odometry, named PDLC-LIO, is developed in this paper towards large-scale environments with close loops. A direct point cloud registration approach without extracting features has been introduced into this framework. Strategies including the pre-integration on the Inertial Measurement Unit (IMU), the direct scan match in local-scale, and an efficient fusion loop closure detection method with conditional detection are included to guarantee the precision. Also, the factor graph optimization is considered, and measurements from LiDAR odometry, IMU and loop closures can thus be integrated into the back-end optimization. The proposed method has been verified on both the public datasets and the test cases collected by our own autonomous driving experiment platform. Accurate experimental results can be obtained. Such results show that the performance including accumulated errors and the drift phenomenon outperforms the state-of-the-art algorithms LeGO-LOAM/ LIO-SAM /FAST-LIO2, and the loop closure detection rate can be promoted up to 75%.