The KITTI Vision Benchmark Suite

Method

[st] Gradient-based Direct Visual Odometry [GDVO]

Submitted on 13 Nov. 2016 10:22 by
Jianke Zhu (Zhejiang University)

Running time:		0.09 s
Environment:		1 core @ >3.5 Ghz (C/C++)

Method Description:

Gradient-based Direct Visual Odometry without local bundle
adjustment and loop closure.

Parameters:

window size 12
4 scales, two channel

Latex Bibtex:

@inproceedings{ijcai17,
author = {Jianke Zhu},
title = {Image Gradient-based Joint Direct Visual Odometry for
Stereo Camera},
booktitle = {International Joint Conference on Artificial Intelligence,
{IJCAI}},
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.