The KITTI Vision Benchmark Suite

Method

D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry [D3VO]

Submitted on 20 Feb. 2019 14:09 by
Nan Yang (TUM)

Running time:		0.1 s
Environment:		1 core @ 2.5 Ghz (C/C++)

Method Description:

Monocular Visual Odometry.

Parameters:

keyframes=7

Latex Bibtex:

@inproceedings{yang2020d3vo,
title={D3VO: Deep Depth, Deep Pose and Deep
Uncertainty for Monocular Visual Odometry},
author={Yang, Nan and Stumberg, von, Lukas and
Wang, Rui and Cremers, Daniel},
booktitle={The IEEE Conference on Computer Vision
and Pattern Recognition (CVPR)},
month={June},
year={2020}
}

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.