The KITTI Vision Benchmark Suite

Method

SOFT2 [st] [SOFT2]

Submitted on 9 Apr. 2021 20:28 by
Igor Cvisic (University of Zagreb Faculty of Electrical Engineering and Computing )

Running time:		0.1 s
Environment:		4 cores @ 2.5 Ghz (C/C++)

Method Description:

Stereo Visual Odometry for Road Vehicles Based on a Point-to-Epipolar-Line Metric

Parameters:

\threshold=0.5

Latex Bibtex:

@article{9835150,
author={Igor Cvišić and Ivan Marković and Ivan Petrović},
journal={IEEE Transactions on Robotics},
title={SOFT2: Stereo Visual Odometry for Road Vehicles Based on a Point-to-Epipolar-Line Metric},
year={2022},
pages={1-16},
doi={10.1109/TRO.2022.3188121}}
@article{CVISIC2022104189,
title = {Enhanced calibration of camera setups for high-performance visual odometry},
journal = {Robotics and Autonomous Systems},
volume = {155},
pages = {104189},
year = {2022},
issn = {0921-8890},
doi = {https://doi.org/10.1016/j.robot.2022.104189},
author = {Igor Cvišić and Ivan Marković and Ivan Petrović},
}
@inproceedings{Cvisic2021,
author = {Cvišić, Igor and Marković, Ivan and Petrović, Ivan},
booktitle = {European Conference on Mobile Robots (ECMR)},
title = {{Recalibrating the KITTI Dataset Camera Setup for Improved Odometry Accuracy}},
year = {2021}
}

Detailed Results

From all test sequences (sequences 11-21), our benchmark computes translational and rotational errors for all possible subsequences of length (5,10,50,100,150,...,400) meters. Our evaluation ranks methods according to the average of those values, where errors are measured in percent (for translation) and in degrees per meter (for rotation). Details for different trajectory lengths and driving speeds can be found in the plots underneath. Furthermore, the first 5 test trajectories and error plots are shown below.