论文/arXiv 2609.12837·1 个月前

低空飞行中空中 LiDAR-惯性里程计的参数敏感性分析

针对低空无人机LIO参数调优困难，本文用网格搜索、相关性与随机森林分析，在多LiDAR低至中等高度飞行数据集上验证。

AI 整理

• 本体 SDK 与控制
• 感知与导航
• 数据与采集

原文链接：Parameter Sensitivity Analysis for Aerial LiDAR-Inertial Odometries in low-altitude flights（arXiv 2609.12837 · 发布日期：2026-09-11）

Parameter Sensitivity Analysis for Aerial LiDAR-Inertial Odometries in low-altitude flights

针对低空无人机LIO参数调优困难，本文用网格搜索、相关性与随机森林分析，在多LiDAR低至中等高度飞行数据集上验证。

核心要点

• LiDAR SLAM与LIO性能受场景、LiDAR和机器人运动影响，常需密集调参。
• 分析FAST-LIO2及Cartographer的LIO模块参数对ATE性能的影响。
• 采用穷举网格搜索生成参数组合，并用Pearson相关和随机森林置换重要性评估。
• 得到关键参数、简化调参流程和建议；94%案例ATE距网格搜索最优不超过5厘米。

验证范围与局限：验证限于使用不同LiDAR在低至中等高度、多环境飞行记录的航拍LiDAR SLAM数据集。

英文摘要 (Abstract)

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.

作者：Robert Milijas, José Ramiro Martínez‐de Dios, Stjepan Bogdan

来源

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1. arXiv 2609.12837：Parameter Sensitivity Analysis for Aerial LiDAR-Inertial Odometries in low-altitude flights访问 2026-10-21