DeepLO: Multi-projection deep LIDAR odometry for space orbital robotics rendezvous relative navigation. (December 2020)

• Record Type: Journal Article
• Authors: Kechagias-Stamatis, O.; Aouf, N.; Dubanchet, V.; Richardson, M.A.
• Is Part Of: Acta astronautica. Volume 177(2020)
• Page Start: 270
• Page End: 285
• Publication Date: 2020-12
• Subjects: Convolutional neural networks -- Deep learning -- LIDAR -- Multi-dimensional processing -- Recurrent neural networks -- Relative navigation -- Robotics; Astronautics -- Periodicals; Outer space -- Exploration -- Periodicals; Astronautics; Periodicals; 629.405
• DOI: 10.1016/j.actaastro.2020.07.034
• Languages: English
• ISSNs: 0094-5765

Abstract

Abstract: This work proposes a new Light Detection and Ranging (LIDAR) based navigation architecture that is appropriate for uncooperative relative robotic space navigation applications. In contrast to current solutions that exploit 3D LIDAR data, our architecture suggests a Deep Recurrent Convolutional Neural Network (DRCNN) that exploits multi-projected imagery of the acquired 3D LIDAR data. Advantages of the proposed DRCNN are; an effective feature representation facilitated by the Convolutional Neural Network module within DRCNN, a robust modeling of the navigation dynamics due to the Recurrent Neural Network incorporated in the DRCNN, and a low processing time. Our trials evaluate several current state-of-the-art space navigation methods on various simulated but credible scenarios that involve a satellite model developed by Thales Alenia Space (France). Additionally, we evaluate real satellite LIDAR data acquired in our lab. Results demonstrate that the proposed architecture, although trained solely on simulated data, is highly adaptable and is more appealing compared to current algorithms on both simulated and real LIDAR data scenarios affording better odometry accuracy at lower computational requirements. Highlights: Deep network trained on synthetic data and evaluated on both synthetic and real data. Effective feature representation for untrained environments via the CNN module. Robust and automatic modeling of the navigation dynamics due to the RNN module. Processing efficient navigation via 3D to 2D data remapping.