LiDAR odometry tuning improves drone flight navigation accuracy
Parameter Sensitivity Analysis for Aerial LiDAR-Inertial Odometries in low-altitude flights
Robotics
Summary
Drones use special sensors like LiDAR and inertial devices to figure out where they are while flying. Getting these sensors to work just right can be tricky because there are many settings to adjust, and it depends on things like the drone’s movement and environment. The paper looks at how changing these settings affects how accurately drones can track their position. The authors studied two popular drone mapping methods and gave clear advice on how to tune their settings to get good accuracy without much trial and error.
What this means in practice
- •For drone navigation engineers: Tune drone LiDAR-inertial algorithms efficiently for precise flight positioning using recommended parameter settings.
- •For mapping system integrators: Improve aerial mapping accuracy by applying simplified tuning guidelines for SLAM and odometry algorithms.
Authors
Robert Milijas, Jose Ramiro Martinez-de Dios, Stjepan Bogdan
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.