PillarFlow: End-to-end Birds-eye-view Flow Estimation for Autonomous Driving

• URL: http://arxiv.org/abs/2008.01179v3
• Date: Sat, 29 Aug 2020 13:35:09 GMT
• Title: PillarFlow: End-to-end Birds-eye-view Flow Estimation for Autonomous Driving
• Authors: Kuan-Hui Lee, Matthew Kliemann, Adrien Gaidon, Jie Li, Chao Fang, Sudeep Pillai, Wolfram Burgard
• Abstract summary: 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.
• Score: 42.8479177012748
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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