The Foreseeable Future: Self-Supervised Learning to Predict Dynamic Scenes for Indoor Navigation

• Published in: IEEE transactions on robotics 2023-12, Vol.39 (6), p.4581-4599
• Main Authors: Thomas, Hugues, Zhang, Jian, Barfoot, Timothy D.
• Format: Article
• Language: English
• Subjects: Adaptive systems; Annotations; Deep learning; Deep learning in robotics and automation; Heuristic algorithms; Indoor navigation; Laser radar; learning and adaptive systems; Lidar; Lifelong learning; Multiagent systems; Navigation; Navigation systems; Prediction algorithms; reactive and sensor-based planning; Reactive power; Robotics and automation; Robots; Self-supervised learning; Semantic segmentation; Semantics; Trajectory
• ISSN: 1552-3098; 1941-0468
• DOI: 10.1109/TRO.2023.3304239

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 autolabeling process that creates SOGMs from noisy real navigation data. We use a 3-D-2-D feedforward architecture, trained to predict the future time steps of SOGMs, given 3-D Lidar frames as input. Our pipeline is entirely self-supervised, thus enabling lifelong learning for real robots. The network is composed of a 3-D back-end that extracts rich features and enables the semantic segmentation of the lidar frames, and a 2-D front-end that predicts the future information embedded in the SOGM representation, potentially capturing the complexities and uncertainties of real-world multiagent interactions. We also design a navigation system that uses these predicted SOGMs within planning, after they have been transformed into spatiotemporal risk maps. 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 3-D lidar dataset, collected during our experiments, which includes our automated annotations.