Method

VOLDOR [VOLDOR]
https://github.com/htkseason/VOLDOR

Submitted on 10 Nov. 2019 08:33 by
Zhixiang Min (Stevens Institute of Technology)

Running time:0.1 s
Environment:GPU

Method Description:
VOLDOR: Visual Odometry From Log-Logistic Dense
Optical Flow Residuals
Parameters:
Latex Bibtex:
@InProceedings{Min_2020_CVPR,
author = {Min, Zhixiang and Yang, Yiding and Dunn,
Enrique},
title = {VOLDOR: Visual Odometry From Log-Logistic
Dense Optical Flow Residuals},
booktitle = {IEEE/CVF 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.

Test Set Average


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Sequence 11


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Sequence 12


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Sequence 13


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Sequence 14


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Sequence 15


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