PillarFlow: End-to-end Birds-eye-view Flow Estimation for Autonomous Driving
Kuan-Hui Lee, Matthew Kliemann, Adrien Gaidon, Jie Li, Chao Fang, Sudeep Pillai, Wolfram Burgard
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.
BibTeX
@inproceedings{iros2020_pillarflowendtoe,
title = {PillarFlow: End-to-end Birds-eye-view Flow Estimation for Autonomous Driving},
author = {Kuan-Hui Lee and Matthew Kliemann and Adrien Gaidon and Jie Li and Chao Fang and Sudeep Pillai and Wolfram Burgard},
booktitle = {IROS 2020},
year = {2020}
}