Distributed Variable-Baseline Stereo SLAM from two UAVs

TL;DR

This paper introduces a distributed, collaborative VIO approach using two UAVs with adjustable baseline to improve high-altitude navigation accuracy beyond current methods.

Contribution

It proposes a novel distributed fusion scheme for forming a virtual stereo rig with adjustable baseline between UAVs for enhanced VIO performance.

Findings

Effective high-altitude navigation up to 160m altitude.

Baseline adjustment reduces estimation error by a factor of two.

Achieves low latency pose estimation of 11ms per agent.

Abstract

VIO has been widely used and researched to control and aid the automation of navigation of robots especially in the absence of absolute position measurements, such as GPS. However, when observable landmarks in the scene lie far away from the robot's sensor suite, as it is the case at high altitude flights, the fidelity of estimates and the observability of the metric scale degrades greatly for these methods. Aiming to tackle this issue, in this article, we employ two UAVs equipped with one monocular camera and one IMU each, to exploit their view overlap and relative distance measurements between them using UWB modules onboard to enable collaborative VIO. In particular, we propose a novel, distributed fusion scheme enabling the formation of a virtual stereo camera rig with adjustable baseline from the two UAVs. In order to control the \gls{uav} agents autonomously, we propose a decentralized collaborative estimation scheme, where each agent hold its own local map, achieving an average pose estimation latency of 11ms, while ensuring consistency of the agents' estimates via consensus based optimization. Following a thorough evaluation on photorealistic simulations, we demonstrate the effectiveness of the approach at high altitude flights of up to 160m, going significantly beyond the capabilities of state-of-the-art VIO methods. Finally, we show the advantage of actively adjusting the baseline on-the-fly over a fixed, target baseline, reducing the error in our experiments by a factor of two.