Prior Guided Dropout for Robust Visual Localization in Dynamic Environments
Prior Guided Dropout for Robust Visual Localization in Dynamic Environments
Zhaoyang Huang, Yan Xu, Jianping Shi, Xiaowei Zhou, Hujun Bao, Guofeng Zhang
License
Licensed under the CC BY-NC-SA 4.0 license, see LICENSE.
Citation
If you find this code useful for your research, please cite our paper
@inproceedings{huang2019prior,
title={Prior Guided Dropout for Robust Visual Localization in Dynamic Environments},
author={Huang, Zhaoyang and Xu, Yan and Shi, Jianping and Zhou, Xiaowei and Bao, Hujun and Zhang, Guofeng},
booktitle={Proceedings of the IEEE International Conference on Computer Vision},
pages={2791--2800},
year={2019}
}
Environment
PGD-MapNet uses Conda to setup the environment
conda env create -f environment.yml
conda activate pgd-mapnet
The data is processed as suggested in geomapnet.
The dynamic information computed from Mask_RCNN is stored in datainfo
.
The files should be put into the corresponding root dir of each scene.
Running
Training
cd experiments
bash runattmapnet.sh
Evaluation
cp logs/exp_beta[-3.0]gamma[-3.0]batch_size[64]model[attentionmapnet]mask_sampling[True]sampling_threshold[0.2]color_jitter[0.0]uniform_sampling[False]mask_image[False]dataset[RobotCar]scene[full]/config.json admapfull.json
bash run_eval.sh
Acknowledgements
Our code partially builds on geomapnet.
The work is affliated with ZJU-SenseTime Joint Lab of 3D Vision, and its intellectual property belongs to SenseTime Group Ltd.