The KITTI Vision Benchmark Suite

Method

Backward Motion for Estimation Enhancement in Sparse Visual Odometry [BVO]

Submitted on 25 Jan. 2017 14:30 by
Fabio Pereira (UFRGS)

Running time:		0.1 s
Environment:		1 core @ 2.5GHz (Python)

Method Description:

Images analyzed in reversed order.

Parameters:

Latex Bibtex:

@INPROCEEDINGS{8278080,
author={F. Pereira and J. Luft and G. Ilha and A. Sofiatti and A. Susin},
booktitle={2017 Workshop of Computer Vision (WVC)},
title={Backward Motion for Estimation Enhancement in Sparse Visual Odometry},
year={2017},
volume={},
number={},
pages={61-66},
keywords={cameras;distance measurement;mobile robots;motion estimation;pose estimation;robot vision;stereo image processing;backward motion;backward movement;camera position;camera translations;drones;estimation enhancement;forward motion application;lower parallax;monocular backward looking camera VO system;optical axis;projection characteristics;sideway translations;sparse features;sparse visual odometry;utonomous robots;Automobiles;Cameras;Feature extraction;Pose estimation;Robot vision systems;Visual odometry;Backward Motion;Bundle Adjustment;Visual Odometry},
doi={10.1109/WVC.2017.00018},
ISSN={},
month={Oct},}

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.