Adaptive bird's eye view description for long-term mapping and loop closure in 3D point clouds

In this paper, we proposed a robust, adaptive bird eye view descriptor for loop closure detection and long term mapping in 3D point cloud. Loop closure detection plays a crucial role in SLAM context, enhancing the quality and the accuracy of constructed point cloud map. While visual-based loop closure detection methods are vulnerable against perceptual and lightness variances, LiDAR-based methods are robust against such variance. Therefore, to construct our BEV descriptor, we accumulate and register each LiDAR scan to form a key-frame, and develop an algorithm to radially and azimuthally partition and encode the key-frame into a 2D BEV pixel image. Then, similarity scores between every BEV descriptor are calculated to find the best candidate for loop closure. The best candidate is further validated through ICP-based geometric verification, and its newly established restriction is the used for pose graph optimization to improve the quality of the point cloud map. In our experiments, we compare our loop closure detection method with Scan Conext, a state-of-art global descriptor, on public dataset (KITTI) and our private dataset (collected with a Livox solid-state LiDAR). The results of both datasets show that our proposed descriptor has stronger adaptability in different types of environment (both indoor and outdoor) and LiDAR, with a greatly improved accuracy and overall performance.