Method

a dEep poiNt Cloud ODometry nEtwork [la] [ENCODE]
https://github.com/stzyhian/ENCODE.git

Submitted on 16 Sep. 2020 07:42 by
Yihuan Zhang (Tsinghua Automotive Research Institute)

Running time:0.08 s
Environment:4 cores @ 2.5 Ghz (Python + C/C++)

Method Description:
We(TSARI) developed a deep neural network based
lidar slam framework, the only input of the
framework is two adjacent frames of point cloud.
The relative pose is learned from the dEep poiNt
Cloud ODometry nEtwork(ENCODE). The result is not
very good right now and we are working on some
improvement. To be continued.
Parameters:
default
Latex Bibtex:
in process

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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