The KITTI Vision Benchmark Suite

Method

Fast Techniques for Monocular Visual Odometry [FTMVO]

Submitted on 26 May. 2015 10:08 by
Mohammad Hossein Mirabdollah (Paderborn university)

Running time:		0.11 s
Environment:		1 core @ 2.5 Ghz (C/C++)

Method Description:

An fast iterative 5-point method is used to
estimate inter-frame camera motion.

A Robust method is proposed to track low quality
features on the ground plane and estimate scale
of translation.

A probabilistic triangulation method is used to
find the positions and uncertainties of features
in space

By tracking features maximally up to 10 frames,
the last camera pose is enhanced by the
minimization of a cost function containing
different constraints for far and close features.

Parameters:

features=300

Latex Bibtex:

@inproceedings {Mirabdollah2015GCPR,
author = { Hossein Mirabdollah and B{"a}rbel
Mertsching },
title = { Fast Techniques for Monocular Visual
Odometry },
booktitle = {Proceeding of 37th
German Conference on Pattern Recognition (GCPR)
},
year = { 2015 },
pages = { 297--307}
}

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.