Method

End-to-End CNN Approach for Visual Odometry [EtELVO]


Submitted on 16 May. 2017 15:29 by
Christoph Rist (FZI Forschungszentrum Informatik)

Running time:0.02 s
Environment:GPU @ 1.0 Ghz (Python + C/C++)

Method Description:
End-to-End CNN-Architecture for regression, trained
on 10 of
11 KITTI Odometry sequences with ground truth.
Outputting change in position and orientation
between two frames, concatenated without any local
optimizations.
Parameters:
\beta=50.0
Latex Bibtex:

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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