融合相机色彩信息提升激光雷达里程计与建图的精度和鲁棒性
CAR-LOAM: Color-Assisted Robust LiDAR Odometry and Mapping
- 利用相机图像为激光点云着色，增强特征匹配可靠性
- 采用感知均匀的颜色差异加权策略，有效剔除颜色异常匹配
- 在复杂森林和校园场景中表现更优，适合高精度环境重建
本文提出一种基于颜色辅助的鲁棒激光雷达里程计与建图框架（CAR-LOAM）。该框架同时接收激光雷达与相机数据，利用相机图像对激光点云进行着色，并在此基础上执行迭代位姿优化。针对每帧激光扫描，提取边缘与平面特征并使用对应图像着色后，与全局地图进行匹配。具体地，采用感知均匀的颜色差异加权策略排除颜色对应异常点，结合基于Welsch函数的鲁棒误差度量抑制位置对应异常点的影响。实验结果表明，该系统实现了高精度定位，并重建出稠密、准确、彩色且三维的环境地图。在复杂森林和校园等挑战性场景下的充分测试显示，本方法相比现有最先进方法具有更高的鲁棒性和准确性。
原文摘要 · Abstract (English)
In this letter, we propose a color-assisted robust framework for accurate LiDAR odometry and mapping (LOAM). Simultaneously receiving data from both the LiDAR and the camera, the framework utilizes the color information from the camera images to colorize the LiDAR point clouds and then performs iterative pose optimization. For each LiDAR scan, the edge and planar features are extracted and colored using the corresponding image and then matched to a global map. Specifically, we adopt a perceptually uniform color difference weighting strategy to exclude color correspondence outliers and a robust error metric based on the Welsch's function to mitigate the impact of positional correspondence outliers during the pose optimization process. As a result, the system achieves accurate localization and reconstructs dense, accurate, colored and three-dimensional (3D) maps of the environment. Thorough experiments with challenging scenarios, including complex forests and a campus, show that our method provides higher robustness and accuracy compared with current state-of-the-art methods.
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