Toolkit for VSLAM and VISLAM evaluation.
For more information, please refer to our project website.
This project is released under the Apache 2.0 license.
Usage:
python3 ismar-score.py --round <round> --is_vislam <is_vislam> --trajectory_base_dir <trajectory_base_dir> --gt_base_dir <gt_base_dir>
Arguments:
<round> SLAM benchmark number of rounds, e.g. 5.
<is_vislam> Set to 1 for VISLAM, set to 0 for VSLAM.
<trajectory_base_dir> SLAM camera trajectory file folders(e.g. ~/MY-SLAM/trajectories/). We support TUM format(timestamp[s] px py pz qx qy qz qw) files.
<gt_base_dir> Path to groundtruth folder, e.g. ~/ISMAR-Dataset/train.
Usage:
./accuracy <groundtruth> <input> <fix scale>
Arguments:
<groundtruth> Path to sequence folder, e.g. ~/VISLAM-Dataset/A0.
<input> SLAM camera trajectory file in TUM format(timestamp[s] px py pz qx qy qz qw).
<fix scale> Set to 1 for VISLAM, set to 0 for VSLAM.
Usage:
./initialization <groundtruth> <input> <has inertial>
Arguments:
<groundtruth> Path to sequence folder, e.g. ~/VISLAM-Dataset/A0.
<input> SLAM camera trajectory file in TUM format(timestamp[s] px py pz qx qy qz qw).
<has inertial> Set to 1 for VISLAM, set to 0 for VSLAM.
Usage:
./robustness <groundtruth> <input> <fix scale>
Arguments:
<groundtruth> Path to sequence folder, e.g. ~/VISLAM-Dataset/A0.
<input> SLAM camera trajectory file in TUM format(timestamp[s] px py pz qx qy qz qw).
<fix scale> Set to 1 for VISLAM, set to 0 for VSLAM.
Usage:
relocalization <groundtruth> <input> <has inertial> <jump detection>
Arguments:
<groundtruth> Path to sequence folder, e.g. ~/VISLAM-Dataset/A0.
<input> SLAM camera trajectory file in TUM format(timestamp[s] px py pz qx qy qz qw).
<has inertial> Set to 1 for VISLAM, set to 0 for VSLAM.
<jump detection> Sensitivity to detect jump when relocalization happened (Default 0.05).
If you are using our codebase or dataset for research, please cite the following publication:
@article{
title = {Survey and Evaluation of Monocular Visual-Inertial SLAM Algorithms for Augmented Reality},
author = {Jinyu Li, Bangbang Yang, Danpeng Chen, Nan Wang, Guofeng Zhang*, Hujun Bao*},
journal = {Journal of Virtual Reality & Intelligent Hardware},
year = {2019},
volume = {1},
number = {4},
pages = {386-410},
url = {http://www.vr-ih.com/vrih/html/EN/10.3724/SP.J.2096-5796.2018.0011/article.html},
doi = {10.3724/SP.J.2096-5796.2018.0011}
}