LaserSAM: Zero-Shot Change Detection Using Visual Segmentation of Spinning LiDAR

This paper presents an approach for applying camera perception techniques to spinning LiDAR data. To improve the robustness of long-term change detection from a 3D LiDAR, range and intensity information are rendered into virtual perspectives using a pinhole camera model. Hue-saturation-value image encoding is used to colourize the images by range and near-IR intensity. The LiDAR's active scene illumination makes it invariant to ambient brightness, which enables night-to-day change detection without additional processing. Using the range-colourized, perspective image allows existing foundation models to detect semantic regions. Specifically, the Segment Anything Model detects semantically similar regions in both a previously acquired map and live view from a path-repeating robot. By comparing the masks in both views, changes in the live scan are detected. Results indicate that the Segment Anything Model accurately captures the shape of arbitrary changes introduced into scenes. The proposed method achieves a segmentation intersection over union of 73.3% when evaluated in unstructured environments and 80.4% when evaluated within the planning corridor. Changes can be detected reliably through day-to-night illumination variations. After pixel-level masks are generated, the one-to-one correspondence with 3D points means that the 2D masks can be used directly to recover the 3D location of the changes. The detected 3D changes are avoided in a closed loop by treating them as obstacles in a local motion planner. Experiments on an unmanned ground vehicle demonstrate the performance of the method.