The KITTI Vision Benchmark Suite

Method

Depth Estimation-based Odometry [EB3DTE+RJMCM]

Submitted on 8 Jun. 2017 20:09 by
Zeyd Boukhers (University of Siegen)

Running time:		1 s
Environment:		1 core @ 2.5 Ghz (Matlab)

Method Description:

The method estimates the depth of each image based
on similar candidates.

**the result might be slightly different from the
published one (automatically deleted from KITTI)
since it is a probabilistic approach.

Parameters:

Default except:

h=Estimated and updated in each frame
f=Estimated
optical center = image center

Latex Bibtex:

@ARTICLE{Boukhers_2017_1,
author = {Boukhers, Zeyd and Shirahama,
Kimiaki and Grzegorzek, Marcin},
journal = {Circuits and Systems for Videos
Technology (TCSVT), IEEE Transaction on},
title = {Example-based 3D Trajectory
Extraction of Objects from 2D Videos},
year = {2017},
volume = {PP},
number = {99},
pages = {1-1},
keywords = {Cameras;Feature
extraction;Sensors;Three-dimensional
displays;Training;Trajectory;Videos;Camera
ego;Object interaction;RJ-MCMC Paticle
Filtering;Trajectory extraction},
doi = {10.1109/TCSVT.2017.2727963},
ISSN = {1051-8215},
month = {}
}

@Article{Boukhers2017,
author={Boukhers, Zeyd
and Shirahama, Kimiaki
and Grzegorzek, Marcin},
title={Less restrictive camera odometry estimation
from monocular camera},
journal={Multimedia Tools and Applications},
year={2017},
month={Sep},
day={27},
issn={1573-7721},
doi={10.1007/s11042-017-5195-7},
url={https://doi.org/10.1007/s11042-017-5195-7}
}

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.