Method

Closet Probability and Feature Grid [la] [CPFG-slam]


Submitted on 1 Apr. 2018 09:38 by
Kaijin Ji (Beijing Institute of Technology)

Running time:0.03 s
Environment:4 cores @ 2.5 Ghz (C/C++)

Method Description:
SLAM algorithm based on Lidar Data.
Parameters:
the resolution of feature grid.
Latex Bibtex:
@inproceedings{ChangShu2018IV,
author = "Kaijin Ji and Huiyan Chen , Huijun Di ,
Jianwei Gong , Guangming Xiong , Jianyong Qi , Tao
Yi",
title = "CPFG-SLAM:a robust Simultaneous Localization
and Mapping based on LIDAR in off-road environment",
booktitle="IEEE Intelligent Vehicles Symposium (IV)",
year="2018"}

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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