The KITTI Vision Benchmark Suite

Method

Direct Sparse Visual Odometry with Stereo Cameras [st] [Stereo DSO]

Submitted on 13 Feb. 2018 19:29 by
Rui Wang (Technical University of Munich)

Running time:		0.1 s
Environment:		1 core @ 3.4 Ghz (C/C++)

Method Description:

Stereo DSO jointly optimizes for all the model
parameters within the active window, including
the intrinsic/extrinsic camera parameters of all
keyframes and depth values of all selected
pixels. Constraints from static stereo and
temporal stereo are integrated into the windowed
bundle adjustment using the coupling factor.

More details:
https://vision.in.tum.de/research/vslam/dso#stere
o_dsolarge-
scale_direct_sparse_visual_odometry_with_stereo_c
ameras
Videos:
https://www.youtube.com/watch?v=A53vJO8eygw&t=27s
https://www.youtube.com/watch?v=BxTLhubqEKg

Parameters:

Visual odometry only; Full resolution; Coupling
factor = 2.

Latex Bibtex:

@inproceedings{wang2017stereo,
title={Stereo dso: Large-scale direct sparse
visual odometry with stereo cameras},
author={Wang, Rui and Schw{\"o}rer, Martin and
Cremers, Daniel},
booktitle={International Conference on Computer
Vision (ICCV), Venice, Italy},
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.