Over the last few years, autonomous vehicles have progressed very rapidly. The odometry technique that estimates displacement from consecutive sensor inputs is an essential technique for autonomous driving. In this article, we propose a fast, robust, and accurate odometry technique. The proposed technique is light detection and ranging (LiDAR)-based direct odometry, which uses a spherical range image (SRI) that projects a three-dimensional point cloud onto a two-dimensional spherical image plane. Direct odometry is developed in a vision-based method, and a fast execution speed can be expected. However, applying LiDAR data is difficult because of the sparsity. To solve this problem, we propose an SRI generation method and mathematical analysis, two key point sampling methods using SRI to increase precision and robustness, and a fast optimization method. The proposed technique was tested with the KITTI dataset and real environments. Evaluation results yielded a translation error of 0.69%, a rotation error of 0.0031°/m in the KITTI training dataset, and an execution time of 17 ms. The results demonstrated high precision comparable with state-of-the-art and remarkably higher speed than conventional techniques.

中文翻译：

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DiLO：用于自动驾驶的基于球面距离图像的直接光检测和测距测距

在过去的几年里，自动驾驶汽车的发展非常迅速。从连续传感器输入估计位移的里程计技术是自动驾驶的一项基本技术。在本文中，我们提出了一种快速、稳健且准确的里程计技术。所提出的技术是基于光检测和测距 (LiDAR) 的直接里程计，它使用球面距离图像 (SRI)，将三维点云投影到二维球面图像平面上。直接里程计是在基于视觉的方法中开发的，并且可以预期快速的执行速度。然而，由于稀疏性，应用 LiDAR 数据很困难。为了解决这个问题，我们提出了一种 SRI 生成方法和数学分析，两种使用 SRI 提高精度和鲁棒性的关键点采样方法，和快速优化方法。所提出的技术在 KITTI 数据集和真实环境中进行了测试。评估结果得出在 KITTI 训练数据集中的平移误差为 0.69%，旋转误差为 0.0031°/m，执行时间为 17 ms。结果显示出与最先进技术相当的高精度和比传统技术显着更高的速度。