Method

PMO / PbT-M2 [PMO / PbT-M2]


Submitted on 4 Jul. 2017 11:14 by
Nolang Fanani (Goethe University of Frankfurt)

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

Method Description:
Monocular Visual Odometry
Parameters:
-
Latex Bibtex:
@article{fanani2017predictive,
title={Predictive monocular odometry (PMO): What is possible without RANSAC and multiframe bundle adjustment?},
author={Fanani, Nolang and Stuerck, Alina and Ochs, Matthias and Bradler, Henry and Mester, Rudolf},
journal={Image and Vision Computing},
year={2017},
publisher={Elsevier}
}

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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