The KITTI Vision Benchmark Suite

Method

Stereo Parallel Tracking and Mapping [st] [S-PTAM]
https://github.com/lrse/sptam

Submitted on 27 Apr. 2016 22:07 by
Taihú Pire (CIFASIS (CONICET-UNR))

Running time:		0.03 s
Environment:		4 cores @ 3.0 Ghz (C/C++)

Method Description:

S-PTAM is a nobel stereo visual SLAM method based
on the monocular PTAM approach. It use stereo
information in tracking and mapping phases.
Furthermore, it exploits the natural parallelism
presented in SLAM.

Parameters:

\feature_detector=GoodFeaturesToTrack
\descriptor_extractor=BRIEF
\matching_distance=25

Latex Bibtex:

@article{pire2017sptam,
title = {{S-PTAM: Stereo Parallel
Tracking and Mapping}},
author = {Pire, Taih{\'u} and Fischer,
Thomas and Castro, Gast{\'o}n and De
Crist{\'o}foris, Pablo and Civera, Javier and
Jacobo Berlles, Julio},
journal = {Robotics and Autonomous
Systems (RAS)},
volume = {93},
pages = {27 -- 42},
year = {2017},
issn = {0921-8890},
doi = {10.1016/j.robot.2017.03.019}
}

@inproceedings{Pire2015IROS,
author = {Taih{\'{u}} Pire and Thomas
Fischer and Javier Civera and Pablo de
Crist{\'{o}}foris and Julio Jacobo{-}Berlles},
title = {Stereo parallel tracking and
mapping for robot localization},
booktitle = IROS,
year = {2015}
}

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.