屋内自律走行のためのLiDARセグメンテーションの自己教師あり学習
Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor Navigation
SLAMとレイトレーシングを組み合わせてLiDAR点群のセマンティックラベルを自動生成し、人手注釈なしで深層セグメンテーションモデルを訓練する手法を提案。走行を重ねるごとに自己改善し、複雑な環境でのナビゲーションや自己位置推定の性能向上を示した。
著者: Hugues Thomas, Ben Agro, Mona Gridseth, Jian Zhang, Timothy D. Barfoot
分類: cs.RO, cs.LG
原文アブストラクト
We present a self-supervised learning approach for the semantic segmentation of lidar frames. Our method is used to train a deep point cloud segmentation architecture without any human annotation. The annotation process is automated with the combination of simultaneous localization and mapping (SLAM) and ray-tracing algorithms. By performing multiple navigation sessions in the same environment, we are able to identify permanent structures, such as walls, and disentangle short-term and long-term movable objects, such as people and tables, respectively. New sessions can then be performed using a network trained to predict these semantic labels. We demonstrate the ability of our approach to improve itself over time, from one session to the next. With semantically filtered point clouds, our robot can navigate through more complex scenarios, which, when added to the training pool, help to improve our network predictions. We provide insights into our network predictions and show that our approach can also improve the performances of common localization techniques.
