The KITTI Vision Benchmark Suite

Method

ROC for Camera-Odometry [st] [ROCC]

Submitted on 15 Dec. 2015 18:18 by
Martin Buczko (rmr, TU Darmstadt)

Running time:		0.3 s
Environment:		2 cores @ 2.0 Ghz (C/C++)

Method Description:

Frame-to-Frame Odometry without BA, SLAM or Loop Closure.

Details can be found on Researchgate.net:

https://www.researchgate.net/publication/305709493_How_to_Distinguish_Inliers_from_Outliers_in_Visual_Odometry_for_High-speed_Automotive_Applications

Parameters:

i_max=30

Latex Bibtex:

@inproceedings{Buczko2016IV,
author = "Martin Buczko and Volker Willert",
title = "How to Distinguish Inliers from Outliers in Visual Odometry for High-speed Automotive Applications",
booktitle="IEEE Intelligent Vehicles Symposium (IV)",
year="2016",
Url="https://www.researchgate.net/publication/305709493_How_to_Distinguish_Inliers_from_Outliers_in_Visual_Odometry_for_High-speed_Automotive_Applications"}

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.