Relative Drone-Ground Vehicle Localization using LiDAR and Fisheye Cameras through Direct and Indirect Observations

TL;DR

This paper introduces a real-time, automatic method for estimating the relative pose between a drone and ground vehicle using LiDAR and fisheye cameras, combining direct drone detection with indirect environmental observations.

Contribution

It presents a novel adaptive kernel-based drone detection method and a rotation correction technique leveraging LiDAR and fisheye camera data for accurate relative localization.

Findings

Achieved fast initial detection of the drone.

Enabled real-time tracking of drone pose.

Demonstrated fully automatic operation.

Abstract

Estimating the pose of an unmanned aerial vehicle (UAV) or drone is a challenging task. It is useful for many applications such as navigation, surveillance, tracking objects on the ground, and 3D reconstruction. In this work, we present a LiDAR-camera-based relative pose estimation method between a drone and a ground vehicle, using a LiDAR sensor and a fisheye camera on the vehicle's roof and another fisheye camera mounted under the drone. The LiDAR sensor directly observes the drone and measures its position, and the two cameras estimate the relative orientation using indirect observation of the surrounding objects. We propose a dynamically adaptive kernel-based method for drone detection and tracking using the LiDAR. We detect vanishing points in both cameras and find their correspondences to estimate the relative orientation. Additionally, we propose a rotation correction technique by relying on the observed motion of the drone through the LiDAR. In our experiments, we were able to achieve very fast initial detection and real-time tracking of the drone. Our method is fully automatic.