The KITTI Vision Benchmark Suite

Method

Visual Odometry by Multi-frame Feature Integration [st] [MFI]

Submitted on 3 Oct. 2013 20:41 by
Hernan Badino (Carnegie Mellon Univeristy)

Running time:		0.1 s
Environment:		1 core @ 2.2 Ghz (C/C++)

Method Description:

VO method based on Multi-frame Feature Integration (MFI). Local features measured and tracked over all past frames are integrated into a
single improved estimate. The method includes a feature outlier rejection and robust LS estimation.

Parameters:

\Detector=GFTT
\Descriptor=FREAK
\Matcher=BRUTEFORCE
\NumTrackedFeatures=4096

Latex Bibtex:

@inproceedings{Badino2013ICCVWORK,
author = "Hern\'{a}n Badino and Akihiro Yamamoto and Takeo Kanade",
title = "Visual Odometry by Multi-frame Feature Integration",
booktitle = "First International Workshop on Computer Vision for Autonomous Driving at ICCV",
year = "2013",
month = "December"
}

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.