Method

SMTD-LiDAR Odometry [la] [SMTD-LO]
[Anonymous Submission]

Submitted on 30 Dec. 2021 10:15 by
[Anonymous Submission]

Running time:0.3 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
Our research project is the 3D LiDAR based
Simultaneous Localization And Mapping(SLAM)
algorithms. Dynamic points are removed, and
feature points are extracted from de-skewed LiDAR
points. A sensor-centric sliding map is
maintained. Extracted features are aligned to the
sliding map to estimated the rigid transformation.
Currently, only planar features are utilized. As a
result, SMTD-LO performs poorly in the high-way
scenario.
Parameters:
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Latex Bibtex:
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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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