フォトリアルなシミュレーションからの深層転移学習によるハードウェア高速化火星サンプル位置推定
Hardware-accelerated Mars Sample Localization via deep transfer learning from photorealistic simulations
火星サンプル回収ミッションに向けて、フォトリアルな3Dシミュレーションで学習した深層学習モデルと転移学習により、火星表面のサンプルチューブを自律的に検出・姿勢推定するシステムを提案し、実機テストで有効性を検証した。
著者: Raúl Castilla-Arquillo, Carlos Jesús Pérez-del-Pulgar, Gonzalo Jesús Paz-Delgado, Levin Gerdes
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
The goal of the Mars Sample Return campaign is to collect soil samples from the surface of Mars and return them to Earth for further study. The samples will be acquired and stored in metal tubes by the Perseverance rover and deposited on the Martian surface. As part of this campaign, it is expected that the Sample Fetch Rover will be in charge of localizing and gathering up to 35 sample tubes over 150 Martian sols. Autonomous capabilities are critical for the success of the overall campaign and for the Sample Fetch Rover in particular. This work proposes a novel system architecture for the autonomous detection and pose estimation of the sample tubes. For the detection stage, a Deep Neural Network and transfer learning from a synthetic dataset are proposed. The dataset is created from photorealistic 3D simulations of Martian scenarios. Additionally, the sample tubes poses are estimated using Computer Vision techniques such as contour detection and line fitting on the detected area. Finally, laboratory tests of the Sample Localization procedure are performed using the ExoMars Testing Rover on a Mars-like testbed. These tests validate the proposed approach in different hardware architectures, providing promising results related to the sample detection and pose estimation.
