Integrated Perception and Planning for Autonomous Vehicle Navigation: An Optimization-Based Approach
Abstract
We propose an optimization-based integrated perception and planning framework for autonomous vehicle navigation that achieves real-time state estimation and path planning with high accuracy and robustness. Our Simultaneous Localization And Mapping (SLAM) module is based on Error-State Extended Kalman Filter (ES-EKF) for LiDAR-Inertial sensor fusion. The SLAM system generates a cost map using Euclidean Distance Transform (EDT) that directly encodes environmental constraints as a cost map. A non-linear trajectory optimization problem is formulated with the cost function and solved in real-time using the direct collocation approach. Our results on the KITTI dataset demonstrate the effectiveness of our framework.
Cite
Text
Kedia et al. "Integrated Perception and Planning for Autonomous Vehicle Navigation: An Optimization-Based Approach." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023. doi:10.1109/CVPRW59228.2023.00323Markdown
[Kedia et al. "Integrated Perception and Planning for Autonomous Vehicle Navigation: An Optimization-Based Approach." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023.](https://mlanthology.org/cvprw/2023/kedia2023cvprw-integrated/) doi:10.1109/CVPRW59228.2023.00323BibTeX
@inproceedings{kedia2023cvprw-integrated,
title = {{Integrated Perception and Planning for Autonomous Vehicle Navigation: An Optimization-Based Approach}},
author = {Kedia, Shubham and Zhou, Yu and Karumanchi, Sambhu H.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2023},
pages = {3206-3215},
doi = {10.1109/CVPRW59228.2023.00323},
url = {https://mlanthology.org/cvprw/2023/kedia2023cvprw-integrated/}
}