Siyu Li, Kailun Yang, Hao Shi, Jiaming Zhang, Jiacheng Lin, Zhifeng Teng, and Zhiyong Li
IEEE Robotics and Automation Letters [PDF]
A semantic map of the road scene, covering fundamental road elements, is an essential ingredient in autonomous driving systems. It provides important perception foundations for positioning and planning when rendered in the Bird’sEye-View (BEV). Currently, the learning of prior knowledge, the hypothetical depth, has two sides in the research of BEV scene understanding. It can guide learning of translating front perspective views into BEV directly on the help of calibration parameters. However, it suffers from geometric distortions in the representation of distant objects. In this paper, we propose a Bi-Mapper framework for top-down road-scene semantic understanding. The dual streams incorporate global view and local prior knowledge, which are learned asynchronously according to the learning timing. At the same time, an Across-Space Loss (ASL) is designed to mitigate the negative impact of geometric distortions. Extensive results verify the effectiveness of each module in the proposed Bi-Mapper framework. Compared with exiting road mapping networks, the proposed Bi-Mapper achieves 5.0 higher IoU on the nuScenes dataset. Moreover, we verify the generalization performance of Bi-Mapper in a realworld driving scenario.
2023.04.28 Init repository. 2024.07.24 Update
Download nuScenes dataset. And change the dataset path in the code.
pip install -r requirement.txt
python train.py
python evalute.py
If you find this repo useful, please consider referencing the following paper:
@article{li2023bimapper,
title={Bi-Mapper: Holistic BEV Semantic Mapping for Autonomous Driving},
author={Li, Siyu and Yang, Kailun and Shi, Hao and Zhang, Jiaming and Lin, Jiacheng and Teng, Zhifeng and Li, Zhiyong},
journal={IEEE Robotics and Automation Letters},
year={2023}
}
The code framework of this project is based on , thanks to this excellent work.
Feel free to contact me if you have additional questions or have interests in collaboration. Please drop me an email at lsynn@hnu.edu.cn