Fusion of Visual-Inertial Odometry with LiDAR Relative Localization for Cooperative Guidance of a Micro-Scale Aerial Vehicle

TL;DR

This paper introduces a cooperative localization method combining LiDAR and Visual-Inertial Odometry to guide a micro-UAV accurately, leveraging the strengths of both sensors for improved navigation in complex environments.

Contribution

A novel cooperative localization approach that fuses LiDAR data with VIO outputs to enhance the guidance of a micro-scale UAV in real-world scenarios.

Findings

Achieved an average 3D ATE of 0.28 m, outperforming raw VIO.

Demonstrated effective guidance of a secondary UAV using combined sensor data.

Enabled exploration of large areas and inaccessible locations with heterogeneous UAVs.

Abstract

A novel relative localization approach for guidance of a micro-scale Unmanned Aerial Vehicle (UAV) by a well-equipped aerial robot fusing Visual-Inertial Odometry (VIO) with Light Detection and Ranging (LiDAR) is proposed in this paper. LiDAR-based localization is accurate and robust to challenging environmental conditions, but 3D LiDARs are relatively heavy and require large UAV platforms, in contrast to lightweight cameras. However, visual-based self-localization methods exhibit lower accuracy and can suffer from significant drift with respect to the global reference frame. To benefit from both sensory modalities, we focus on cooperative navigation in a heterogeneous team of a primary LiDAR-equipped UAV and a secondary micro-scale camera-equipped UAV. We propose a novel cooperative approach combining LiDAR relative localization data with VIO output on board the primary UAV to obtain an accurate pose of the secondary UAV. The pose estimate is used to precisely and reliably guide the secondary UAV along trajectories defined in the primary UAV reference frame. The experimental evaluation has shown the superior accuracy of our method to the raw VIO output, reaching the average 3D Absolute Trajectory Error (ATE) of 0.28 m, and demonstrated its capability to guide the secondary UAV along desired trajectories while mitigating VIO drift. Thus, such a heterogeneous system can explore large areas with LiDAR precision, as well as visit locations inaccessible to the large LiDAR-carrying UAV platforms, as was showcased in a real-world cooperative mapping scenario.