Robust and accurate pose estimation of a robotic platform, so-called sensor-based odometry, is an essential part of many robotic applications. While many sensor odometry systems made progress by adding more complexity to the ego-motion estimation process, we move in the opposite direction. By removing a majority of parts and focusing on the core elements, we obtain a surprisingly effective system that is simple to realize and can operate under various environmental conditions using different LiDAR sensors. Our odometry estimation approach relies on point-to-point ICP combined with adaptive thresholding for correspondence matching, a robust kernel, a simple but widely applicable motion compensation approach, and a point cloud subsampling strategy. This yields a system with only a few parameters that in most cases do not even have to be tuned to a specific LiDAR sensor. Our system using the same parameters performs on par with state-of-the-art methods under various operating conditions using different platforms: automotive platforms, UAV-based operation, vehicles like segways, or handheld LiDARs. We do not require integrating IMU information and solely rely on 3D point cloud data obtained from a wide range of 3D LiDAR sensors, thus, enabling a broad spectrum of different applications and operating conditions. Our open-source system operates faster than the sensor frame rate in all presented datasets and is designed for real-world scenarios.

翻译：鲁棒且精确的机器人平台位姿估计（即基于传感器的里程计）是众多机器人应用的核心组成部分。尽管许多传感器里程计系统通过增加运动估计过程的复杂性取得了进展，我们却反其道而行之。通过消除大部分冗余组件并聚焦于核心要素，我们获得了一个简单易实现、可在多种环境条件下使用不同激光雷达传感器运行的惊人高效系统。本里程估计算法基于点到点ICP，结合自适应阈值对应匹配、鲁棒核函数、简单且广泛适用的运动补偿策略以及点云降采样方法。由此构建的系统仅需少量参数，且在多数情况下甚至无需针对特定激光雷达传感器进行调参。采用相同参数的系统在多种操作条件下（包括汽车平台、无人机平台、赛格威等车辆及手持激光雷达）均能达到与现有最优方法相当的性能。我们无需集成惯性测量单元（IMU）信息，仅依赖各类3D激光雷达传感器获取的三维点云数据，从而支持广泛的应用场景和操作条件。本开源系统在所有测试数据集上的处理速度均超过传感器帧率，专为实际场景设计。