Data-Efficient Collaborative Decentralized Thermal-Inertial Odometry


If you use this work in your research, please cite the following paper:

    author={Polizzi, Vincenzo and Hewitt, Robert and Hidalgo-Carrió, Javier and Delaune, Jeff and Scaramuzza, Davide}, 
    journal={IEEE Robotics and Automation Letters},
    title={Data-Efficient Collaborative Decentralized Thermal-Inertial Odometry}, 


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.

Mars render credits: Mars Ingenuity Helicopter, 3D Model NASA/JPL-Caltech, Mars soil3D reconstruction by Mastcam-Z is licensed under Creative Commons Attribution, rendering Vincenzo Polizzi.