Method

3DOF lidar slam [3DOF-SLAM]


Submitted on 6 Apr. 2020 19:11 by
Martin Dimitrievski (IPI/TELIN)

Running time:0.02 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
Robust Matching of Occupancy Maps for Odometry in Autonomous Vehicles
Parameters:
3DOF
Latex Bibtex:
@conference{visapp16,
author={Martin Dimitrievski. and David Van Hamme. and Peter Veelaert. and Wilfried Philips.},
title={Robust Matching of Occupancy Maps for Odometry in Autonomous Vehicles},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={626-633},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005719006260633},
isbn={978-989-758-175-5},
}

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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