低空飞行中机载LiDAR-惯性里程计的参数敏感性分析
Parameter Sensitivity Analysis for Aerial LiDAR-Inertial Odometries in low-altitude flights
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中文总结 AI 辅助
本文针对低空飞行中的机载LiDAR-惯性里程计（FAST-LIO2和Cartographer），通过网格搜索、Pearson相关性和随机森林置换重要性分析，识别关键参数并给出简化调参建议，使94%案例的ATE误差在最优值5厘米内。
中文摘要 AI 辅助
基于LiDAR的SLAM（同步定位与建图）和LIO（LiDAR-惯性里程计）算法通常用于无人飞行器的精确导航，尤其是在与飞行机器人环境交互时。然而，这些算法的性能在很大程度上依赖于场景、LiDAR和机器人运动特性，通常需要密集的调参过程才能达到期望的性能。为辅助这些调参工作，本文分析了基于EKF的LIO算法（FAST-LIO2）和基于图的SLAM算法的LIO模块（Cartographer）的参数对性能的影响，使用的数据集为在不同环境中低至中等高度飞行时使用不同LiDAR记录的机载LiDAR SLAM数据集。分析基于绝对轨迹误差（ATE），该误差通过使用在穷举网格搜索中获得的每种参数组合配置的LIO算法处理数据集得到。使用Pearson相关性评估单个参数与ATE结果之间的关系，同时使用随机森林置换重要性分析评估每个参数的影响，其中随机森林模型根据参数选择训练以预测产生的ATE值。所进行的分析为Cartographer和FAST-LIO2获得了：i）识别对性能影响较大的参数，ii）简化的调参流程，以及iii）调参建议。使用所提出的调参建议，两种算法在分析的数据集上，在94%的分析案例中获得的ATE值在网格搜索过程中发现的最优性能的5厘米以内。
英文摘要
LiDAR-based SLAM (Simultaneous Localization and Mapping) and LIO (LiDAR-inertial odometry) algorithms are often used for precise navigation of unmanned aerial vehicles, especially during interactions with the aerial robot's environment. However, the performance of these algorithms is greatly dependent on the scenario, LiDAR, and robot motion characteristics, often requiring an intensive tuning process to achieve the desired performance. To aid these tuning efforts, this paper analyzes the influence on performance of the parameters of an EKF-based LIO algorithm (FAST-LIO2) and the LIO module of a graph-based SLAM algorithm (Cartographer) on aerial LiDAR SLAM datasets recorded using different LiDARs in low-to-moderate-altitude flights in diverse environments. The analysis is conducted on the absolute trajectory error (ATE) resulting from processing the datasets with the LIO algorithms configured with each combination of parameters obtained in an exhaustive grid search. The relationship between individual parameters and the ATE results is assessed using Pearson's correlation, while the influence of each parameter is assessed using random forest permutation importance analyses with random forest models trained to predict the resulting ATE values based on the choice of parameters. The performed analysis obtains for Cartographer and FAST-LIO2: i) the identification of parameters with stronger influence in performance, ii) a simplified tuning procedure, and iii) tuning recommendations. Using the proposed tuning recommendations, both algorithms obtain on the analyzed datasets ATE values within 5 cm to the optimal performance found in the grid search procedure in 94% of the analyzed cases.
发表机构
- CoE MARBLE - Centre of Excellence in Maritime Robotics and Technologies for Sustainable Blue Economy（MARBLE卓越中心 - 可持续蓝色经济海事机器人与技术卓越中心）
- Universidad de Sevilla（塞维利亚大学）
- University of Zagreb（萨格勒布大学）
机构由 AI 辅助整理，请以论文原文为准。
