PillarFlow: 自動運転のためのエンドツーエンド俯瞰視点フロー推定
PillarFlow: End-to-end Birds-eye-view Flow Estimation for Autonomous Driving
連続するLiDAR点群から俯瞰視点の2次元フローをエンドツーエンドで推定し、動的・静的物体の追跡性能を向上させる手法を提案した。
著者: Kuan-Hui Lee, Matthew Kliemann, Adrien Gaidon, Jie Li, Chao Fang, Sudeep Pillai, Wolfram Burgard
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
In autonomous driving, accurately estimating the state of surrounding obstacles is critical for safe and robust path planning. However, this perception task is difficult, particularly for generic obstacles/objects, due to appearance and occlusion changes. To tackle this problem, we propose an end-to-end deep learning framework for LIDAR-based flow estimation in bird's eye view (BeV). Our method takes consecutive point cloud pairs as input and produces a 2-D BeV flow grid describing the dynamic state of each cell. The experimental results show that the proposed method not only estimates 2-D BeV flow accurately but also improves tracking performance of both dynamic and static objects.
