The KITTI Vision Benchmark Suite

Method

Stereo Visual Odometry 3pt [st] [VO3pt]

Submitted on 15 Mar. 2012 10:45 by
Pablo Fernández Alcantarilla (Université d'Auvergne)

Running time:		0.56 s
Environment:		1 core @ 2.0 Ghz (C/C++)

Method Description:

Stereo Visual Odometry using a 3 point algorithm and RANSAC for computing the relative motion between consecutive frames. We use the Levenberg-Marquardt algorithm for the nonlinear optimization.

Parameters:

npoints = 3

Latex Bibtex:

@phdthesis{Alcantarilla11phd,
author = {P. F. Alcantarilla},
title = {Vision Based Localization: From Humanoid Robots to Visually Impaired People},
month = {October},
year = {2011},
school = {University of Alcalá},
address = {Alcalá de Henares, Madrid, Spain}
}

@inproceedings{Alcantarilla12icra,
author = {P.F. Alcantarilla and J.J. Yebes and J. Almazán and L.M. Bergasa},
title = {On Combining Visual SLAM and Dense Scene Flow to Increase the Robustness of Localization and Mapping in Dynamic Environments},
booktitle = {ICRA},
location = {St. Paul, USA},
year = {2012}
}

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.