The KITTI Vision Benchmark Suite

Method

[st] MonoROCC [MonoROCC]

Submitted on 11 Jan. 2017 15:17 by
Martin Buczko (rmr, TU Darmstadt)

Running time:		1 s
Environment:		2 cores @ 2.0 Ghz (C/C++)

Method Description:

The outlier rejection pipeline is based on a monocular method, that will be presented at IV17.

The surrounding estimation is performed with stereo methods. That is why this method is tagged as stereo-approach.

Details to our algorithm can be found here:
https://www.researchgate.net/publication/315669432_Monocular_Outlier_Detection_for_Visual_Odometry

Parameters:

n=10

Latex Bibtex:

@inproceedings{Buczko2017IV,
author = "Martin Buczko and Volker Willert",
title = "Monocular Outlier Detection for Visual Odometry",
booktitle=" IEEE Intelligent Vehicles Symposium (IV)",
year="2017",}

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.