In unstructured outdoor environments, robotics requires accurate and efficient odometry with low computational time. Existing low-bias LiDAR odometry methods are often computationally expensive. To address this problem, we present a lightweight LiDAR odometry method that converts unorganized point cloud data into a spherical range image (SRI) and filters out surface, edge, and ground features in the image plane. This substantially reduces computation time and the required features for odometry estimation in LOAM-based algorithms. Our odometry estimation method does not rely on global maps or loop closure algorithms, which further reduces computational costs. Experimental results generate a translation and rotation error of 0.86\% and 0.0036{\deg}/m on the KITTI dataset with an average runtime of 78ms. In addition, we tested the method with our data, obtaining an average closed-loop error of 0.8m and a runtime of 27ms over eight loops covering 3.5Km.

翻译：摘要：在非结构化户外环境中，机器人需要精确高效且计算时间低的里程计方法。现有低偏差激光雷达里程计算法通常计算成本高昂。为解决此问题，我们提出一种轻量级激光雷达里程计方法，该方法将非结构化点云数据转换为球面距离图像，并在图像平面上滤除表面、边缘和地面特征。这显著降低了LOAM类算法中里程计估计所需的计算时间和特征量。我们的里程计估计方法不依赖全局地图或闭环算法，进一步降低了计算成本。实验结果表明，在KITTI数据集上，该方法平移误差为0.86%，旋转误差为0.0036°/m，平均运行时间为78毫秒。此外，我们使用自采集数据对该方法进行测试，在覆盖3.5公里的八次闭环中，平均闭环误差为0.8米，运行时间为27毫秒。