The KITTI Vision Benchmark Suite

Method

Stereo Visual Odometry with Flow Separation + Local Bundle Adjustment [st] [VOFSLBA]

Submitted on 15 Mar. 2012 10:58 by
Pablo Fernández Alcantarilla (Université d'Auvergne)

Running time:		0.52 s
Environment:		1 core @ 2.0 Ghz (C/C++)

Method Description:

Stereo Visual Odometry with flow separation and local bundle adjustment. We use the Levenberg-Marquardt algorithm for the nonlinear optimization.

Firstly the rotation is estimated with a 2-point RANSAC algorithm using the distant features that are farther than a distance threshold (50 m). Then, with the estimated rotation, the translation is estimated with the closer features using a 1-point RANSAC approach.

A Local Bundle Adjusment sliding window of 5 frames is used to refine the most recent 3 frames.

Parameters:

Flow Separation
depth_threshold = 50 m

Local Bundle Adjustment Window
Nk=5
nk=3

Latex Bibtex:

@inproceedings{Kaess09icra,
author = {Michael Kaess and Kai Ni and Frank Dellaert},
title = {Flow separation for fast and robust stereo odometry},
booktitle = ICRA,
address = {Kobe, Japan},
year = {2009}
}

@inproceedings{Alcantarilla10icra,
author = {P.F. Alcantarilla and L.M. Bergasa and F. Dellaert},
title = {Visual Odometry priors for robust {EKF-SLAM}},
booktitle = ICRA,
address = {Anchorage, AK, USA},
year = {2010},
pages = {3501--3506}
}

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.