Data-Efficient Collaborative Decentralized Thermal-Inertial Odometry

TL;DR

This paper introduces a data-efficient decentralized thermal-inertial odometry system for flying robots, enhancing trajectory accuracy while minimizing communication and computational costs through innovative calibration, fusion, and data exchange strategies.

Contribution

It presents a novel decentralized approach combining photometric calibration, covariance-intersection fusion, and VLAD-based communication for thermal-inertial odometry in flying robots.

Findings

Up to 46% improvement in trajectory estimation accuracy.

Up to 89% reduction in communication exchange.

Validated on synthetic and real-world datasets.

Abstract

We propose a system solution to achieve data-efficient, decentralized state estimation for a team of flying robots using thermal images and inertial measurements. Each robot can fly independently, and exchange data when possible to refine its state estimate. Our system front-end applies an online photometric calibration to refine the thermal images so as to enhance feature tracking and place recognition. Our system back-end uses a covariance-intersection fusion strategy to neglect the cross-correlation between agents so as to lower memory usage and computational cost. The communication pipeline uses Vector of Locally Aggregated Descriptors (VLAD) to construct a request-response policy that requires low bandwidth usage. We test our collaborative method on both synthetic and real-world data. Our results show that the proposed method improves by up to 46 % trajectory estimation with respect to an individual-agent approach, while reducing up to 89 % the communication exchange. Datasets and code are released to the public, extending the already-public JPL xVIO library.