Visual Localization Under Appearance Change: A Filtering Approach(DICTA 2019 Best paper) https://arxiv.org/abs/1811.08063
MATLAB code of our DICTA 2019 paper:
"Visual Localization Under Appearance Change: A Filtering Approach" - DICTA 2019 (Best paper award). Anh-Dzung Doan, Yasir Latif, Thanh-Toan Do, Yu Liu, Shin-Fang Ch’ng, Tat-Jun Chin, and Ian Reid. [pdf]
If you use/adapt our code, please kindly cite our paper.
- VLFeat library, version 0.9.21 (http://www.vlfeat.org/)
- yael library (http://yael.gforge.inria.fr/)
- Piotr's Computer Vision Matlab Toolbox (https://pdollar.github.io/toolbox/)
- Some codes adapted from Akihiko Torii and Relja Arandjelovic (http://www.ok.ctrl.titech.ac.jp/~torii/project/247/)
We included, compiled and tested all 3rd-party libraries on MATLAB R2018a, Ubuntu 16.04 LTS 64 bit
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For precomputed features, please download work_dir.zip from here and unzip it to the source code's directory.
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If you want to extract features from original images, please download original images from here.
- Unzip
2014-06-26-08-53-56.zip,2014-06-26-09-24-58.zip, and2014-06-23-15-41-25.ziptodataset/alternate/ - Unzip
2014-11-28-12-07-13.zip,2014-12-02-15-30-08.zip, and2014-12-09-13-21-02.ziptodataset/full/
- Unzip
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Projection and whitening matrices are adapted from DenseVLAD paper of Torii et al. (http://www.ok.ctrl.titech.ac.jp/~torii/project/247/)
Currently, we only publish the code to test Oxford RobotCar dataset with alternate (1km) and full (10km) routes. We will publish code for GTA dataset soon
Run extractFeatures.m to extract features.
Note that please change the route variable to alternate (1km) or full (10km)
Run doLocalization.m to perform visual localization.
Note that please change the route variable to alternate (1km) or full (10km)
After finishing, it will show mean/median errors, and plot the predicted trajectory same as Figure 8d and 8g within the paper
If you have any questions, feel free to contact me