Feature- and Distribution-Based LiDAR SLAM With Generalized Feature Representation and Heuristic Nonlinear Optimization

In this article, we propose a novel 3-D light detection and ranging (LiDAR) simultaneous localization and mapping (SLAM) method, which contains feature-based fast scan matching, distribution-based keyframe matching, and loop closure. First, by incorporating feature-based and distribution-based LiDAR scan matching into our SLAM system, we combine the advantages of the two mechanisms and formulate a more efficient and precise front end. Second, considering the local surface reconstructing errors caused by the existing feature representation mechanisms, we propose a generalized representation mechanism for geometry feature representation within the distribution-based framework. Third, due to the existence of noisy point correspondences during the pose estimation optimization process, we propose a novel heuristic knowledge to distinguish high-quality matching error items and put more weights on them during the iterative objective function construction process. Extensive experiments are conducted on public and custom datasets, including KITTI, Mulran, NCLT, Stevens, and USTC-VLP16 datasets collected by our mobile platform. The experimental results demonstrate that the proposed mechanisms can effectively promote the LiDAR SLAM system performance and demonstrate competitive performance compared with state-of-the-art methods. We will make our source code open to serve as a new baseline for the robotic community.