基于点级去畸变后不确定性的振动感知激光雷达-惯性里程计
Vibration-aware Lidar-Inertial Odometry based on Point-wise Post-Undistortion Uncertainty
AI总结：
本文提出一种基于点级去畸变后不确定性的振动感知LIO方法，通过建模振动引起的去畸变误差并利用不确定性指导点云匹配与状态更新，有效提升了剧烈振动环境下的里程计精度。
摘要：
在非结构化地形上高速移动的地面机器人会产生剧烈的高频振动，导致激光雷达-惯性里程计（LIO）中的激光雷达扫描畸变。由于（1）剧烈振动期间快速且非平滑的状态变化，以及（2）不可预测的IMU噪声与有限的IMU采样频率，实现准确且高效的去畸变极具挑战性。为解决此问题，本文引入了去畸变后不确定性。首先，我们建模由线性和角振动引起的去畸变误差，并为每个点分配去畸变后不确定性。随后，我们利用该不确定性指导点到地图的匹配，计算不确定性感知残差，并使用迭代卡尔曼滤波器更新里程计状态。我们在多个公开数据集及自行录制的数据上进行了振动平台和移动平台实验，结果表明当激光雷达遭受剧烈振动时，本方法取得了优于其他方法的性能。
英文摘要：
High-speed ground robots moving on unstructured terrains generate intense high-frequency vibrations, leading to LiDAR scan distortions in Lidar-inertial odometry (LIO). Accurate and efficient undistortion is extremely challenging due to (1) rapid and non-smooth state changes during intense vibrations and (2) unpredictable IMU noise coupled with a limited IMU sampling frequency. To address this issue, this paper introduces post-undistortion uncertainty. First, we model the undistortion errors caused by linear and angular vibrations and assign post-undistortion uncertainty to each point. We then leverage this uncertainty to guide point-to-map matching, compute uncertainty-aware residuals, and update the odometry states using an iterated Kalman filter. We conduct vibration-platform and mobile-platform experiments on multiple public datasets as well as our own recordings, demonstrating that our method achieves better performance than other methods when LiDAR undergoes intense vibration.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
