地中レーダによる地表地形分類の学習
Learning Surface Terrain Classifications from Ground Penetrating Radar
地中レーダ(GPR)の信号から機械学習で地表の地形を分類する手法を提案し、4種類の地形データセットを構築して有効性を示した。さらにマルチモーダル意味マッピングに統合し、移動ロボットでの実用性を実証した。
著者: Anja Sheppard, Jason Brown, Nilton Renno, Katherine A. Skinner
分類: cs.RO
原文アブストラクト
Terrain classification is an important problem for mobile robots operating in extreme environments as it can aid downstream tasks such as autonomous navigation and planning. While RGB cameras are widely used for terrain identification, vision-based methods can suffer due to poor lighting conditions and occlusions. In this paper, we propose the novel use of Ground Penetrating Radar (GPR) for terrain characterization for mobile robot platforms. Our approach leverages machine learning for surface terrain classification from GPR data. We collect a new dataset consisting of four different terrain types, and present qualitative and quantitative results. Our results demonstrate that classification networks can learn terrain categories from GPR signals. Additionally, we integrate our GPR-based classification approach into a multimodal semantic mapping framework to demonstrate a practical use case of GPR for surface terrain classification on mobile robots. Overall, this work extends the usability of GPR sensors deployed on robots to enable terrain classification in addition to GPR's existing scientific use cases.
