We propose a novel real-time LiDAR intensity image-based simultaneous localization and mapping method , which addresses the geometry degeneracy problem in unstructured environments. Traditional LiDAR-based front-end odometry mostly relies on geometric features such as points, lines and planes. A lack of these features in the environment can lead to the failure of the entire odometry system. To avoid this problem, we extract feature points from the LiDAR-generated point cloud that match features identified in LiDAR intensity images. We then use the extracted feature points to perform scan registration and estimate the robot ego-movement. For the back-end, we jointly optimize the distance between the corresponding feature points, and the point to plane distance for planes identified in the map. In addition, we use the features extracted from intensity images to detect loop closure candidates from previous scans and perform pose graph optimization. Our experiments show that our method can run in real time with high accuracy and works well with illumination changes, low-texture, and unstructured environments.

翻译：我们提出了一种新颖的基于LiDAR强度图像的实时同步定位与地图构建方法，该方法解决了非结构化环境中的几何退化问题。传统的基于LiDAR的前端里程计主要依赖点、线、面等几何特征。当环境中缺乏这些特征时，可能导致整个里程计系统失效。为避免此问题，我们从LiDAR生成的点云中提取与LiDAR强度图像中识别出的特征相匹配的特征点，然后利用这些提取的特征点进行扫描配准并估计机器人自运动。在后端处理中，我们联合优化对应特征点之间的距离，以及地图中识别出的平面对应的点到平面的距离。此外，我们利用强度图像中提取的特征检测先前扫描中的闭环候选点，并执行位姿图优化。实验结果表明，我们的方法能够在高精度下实时运行，并且能良好适应光照变化、低纹理以及非结构化环境。