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 colourized, perspective range 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 is capable of accurately capturing the shape of arbitrary changes introduced into scenes. The system achieves an object recall of 82.6% and a precision of 47.0%. Changes can be detected through day-to-night illumination variations reliably. After pixel-level masks are generated, the one-to-one correspondence with 3D points means that the 2D masks can be directly used to recover the 3D location of the changes. Eventually, the detected 3D changes can be avoided by treating them as obstacles in a local motion planner.

翻译：本文提出一种将相机感知技术应用于旋转激光雷达数据的方法。为提升基于三维激光雷达的长期变化检测鲁棒性，采用针孔相机模型将距离和强度信息渲染为虚拟视角。通过色调-饱和度-值图像编码技术，依据距离和近红外强度对图像进行着色。激光雷达主动场景照明特性使其不受环境亮度影响，无需额外处理即可实现昼夜变化检测。利用着色后的透视距离图像，可借助现有基础模型检测语义区域。具体而言，Segment Anything模型在预先采集的地图与沿固定路径重复运行的机器人实时视图中同步检测语义相似区域。通过对比两视角的掩膜，实时扫描中的变化得以识别。实验结果表明，Segment Anything模型能够精确捕捉场景中任意引入变化的形状。该系统实现了82.6%的物体召回率与47.0%的精确率，可稳定检测昼夜光照变化下的场景改变。生成像素级掩膜后，由于二维掩膜与三维点云存在一一对应关系，可直接利用二维掩膜恢复变化的3D位置。最终，在局部运动规划器中将检测到的3D变化视为障碍物进行规避处理。