予見可能な未来：屋内ナビゲーションのための動的シーン予測自己教師あり学習
The Foreseeable Future: Self-Supervised Learning to Predict Dynamic Scenes for Indoor Navigation
3D LiDARから将来の時空間占有グリッドマップを自己教師あり学習で予測し、屋内ナビゲーションに活用する手法を提案。
著者: Hugues Thomas, Jian Zhang, Timothy D. Barfoot
分類: cs.RO
原文アブストラクト
We present a method for generating, predicting, and using Spatiotemporal Occupancy Grid Maps (SOGM), which embed future semantic information of real dynamic scenes. We present an auto-labeling process that creates SOGMs from noisy real navigation data. We use a 3D-2D feedforward architecture, trained to predict the future time steps of SOGMs, given 3D lidar frames as input. Our pipeline is entirely self-supervised, thus enabling lifelong learning for real robots. The network is composed of a 3D back-end that extracts rich features and enables the semantic segmentation of the lidar frames, and a 2D front-end that predicts the future information embedded in the SOGM representation, potentially capturing the complexities and uncertainties of real-world multi-agent, multi-future interactions. We also design a navigation system that uses these predicted SOGMs within planning, after they have been transformed into Spatiotemporal Risk Maps (SRMs). We verify our navigation system's abilities in simulation, validate it on a real robot, study SOGM predictions on real data in various circumstances, and provide a novel indoor 3D lidar dataset, collected during our experiments, which includes our automated annotations.
