The KITTI Vision Benchmark Suite

Method

No Temporal Filtering [st] [NOTF]

Submitted on 13 Jun. 2016 11:14 by
Joerg Deigmoeller (Honda Research Institute Europe GmbH)

Running time:		0.45 s
Environment:		1 core @ 3.0 Ghz (C/C++)

Method Description:

Method uses only feature tracking based on Lucas-Kanade Optical Flow, no Bundle-Adjustment. Features are rejected based on geometric meaningful conditions. Ego-motion is estimated using standard RANSAC and P6P.

Parameters:

maximal 4000 features

Latex Bibtex:

@inproceedings{Deigmoeller2016GCPR,
author = "Joerg Deigmoeller and Julian Eggert",
title = "Stereo Visual Odometry without Temporal Filtering",
booktitle="German Conference on Pattern Recognition (GCPR)",
year="2016",}

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.