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.

翻译：摘要：本文提出了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3D激光雷达的长期变化检测鲁棒性，利用针孔相机模型将距离与强度信息渲染至虚拟视角。采用色调-饱和度-值图像编码方式，根据距离和近红外强度对图像进行着色。激光雷达的主动场景照明特性使其对环境亮度不敏感，从而无需额外处理即可实现夜间到白天的变化检测。使用距离着色的透视图像使现有基础模型能够检测语义区域。具体而言，Segment Anything模型在先前采集的地图与重复路径机器人的实时视图中同时检测语义相似区域。通过对比两视角的掩膜，可检测实时扫描中的变化。结果表明，Segment Anything模型能准确捕获场景中任意变化的形状。该方法在非结构化环境中的分割交并比达到73.3%，在规划走廊内的评估结果为80.4%。在昼夜光照变化条件下仍可可靠检测变化。生成像素级掩膜后，由于与3D点存在一一对应关系，2D掩膜可直接用于恢复变化的3D位置。通过将检测到的3D变化视为局部运动规划器中的障碍物，可在闭环中规避这些变化。无人地面车辆实验验证了该方法的性能。