This repository is a PyTorch implementation of Deep Auxiliary Learning for Visual Localization and Odometry, known as VLocNet.

This is NOT an official version published by the authors, but a self-implemented project (with some codes referring to the project of MapNet). It should be mentioned that the current performance is still subpar compared with the reported by the paper, although our best have been tried, but close to the MapNet, on 7Scenes dataset.

We release this code mainly for two purpose:

• Providing a bedrock for those who also endeavor to implement this work
• Encouraging the guys interested in our work to find the bugs if existing and explore the tricks before the code released by the authors.

Feel free to create new issues about our work, and share your ideas and suggestions.

1. Install miniconda with Python 3.5+.
2. Create the mapnet Conda environment: conda env create -f environment.yml.
3. Activate the environment: conda activate mapnet_release.

We only support the 7Scenes dataset for now. The datasets live in the datasets directory. We provide skeletons with symlinks to get you started. Let us call your 7Scenes download directory 7SCENES_DIR. You will need to make the following symlinks:

cd datasets && ln -s 7SCENES_DIR 7Scenes

The python training script and test script are included in train.py and test.py separately. The configuration is straightforward and for simplicity some bool options are replaced by int ( 0 for False and 1 for True).
The training and testing can be carried out by the commands python train.py and python test.py. The specific configurations are at your discretion.