Intensity Triangle Descriptor Constructed From High-Resolution Spinning LiDAR Intensity Image for Loop Closure Detection

LiDAR-based loop closure detection is a crucial part of realizing robust SLAM algorithms for intelligent vehicles with LiDAR sensors. Existing methods often reduce the keypoint dimension to encode the global descriptor, which sacrifices the freedom of loop detection and correction. Based on the 6-DOF rigid transformation property of spatial triangles, we propose an algorithm for extracting and describing 3D keypoints from high-resolution spinning LiDAR intensity images to encode triangle descriptors, termed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">intensity triangle descriptor (ITD)</i>. In comparison to the direct extraction of keypoints from the point cloud, the use of image-derived feature points provides additional photometric texture information and better handles uneven spatial density of the point cloud, which is advantageous in unstructured and geometrically degraded scenes. To enhance the stability of keypoints, the spatial positions of multi-frame image feature points are registered to a keyframe by an odometer for voxel downsampling and non-maximum suppression, with the objective of reducing unstable feature points. For high discrimination, the neighbor image patches of each vertex (keypoint) are aggregated to estimate a Gaussian mixture model (GMM) as the keypoint signature. An efficient two-stage loop closure detection method is then proposed for ITD, consisting of candidate retrieval based on triangle side lengths and vertex GMMs, followed by geometric verification of matched descriptor pairs. The effectiveness of the proposed method is evaluated on the STheReO, FusionPortable, and our self-collected datasets.