The KITTI Vision Benchmark Suite

Method

semanticLegoLOAM [la] [lc] [SLL]

Submitted on 1 Apr. 2022 10:46 by
Shuang Gao (OPPO)

Running time:		0.1 s
Environment:		1 core @ 2.5 Ghz (C/C++)

Method Description:

the method is based on the legoLOAM with semantic
information loop closure optimization

Parameters:

score = 0.3

Latex Bibtex:

@article{DBLP:journals/corr/abs-2108-08516,
author = {Yuhao Zhou and
Huanhuan Fan and
Shuang Gao and
Yuchen Yang and
Xudong Zhang and
Jijunnan Li and
Yandong Guo},
title = {Retrieval and Localization with
Observation Constraints},
journal = {CoRR},
volume = {abs/2108.08516},
year = {2021},
url = {https://arxiv.org/abs/2108.08516},
eprinttype = {arXiv},
eprint = {2108.08516},
timestamp = {Mon, 23 Aug 2021 14:07:13 +0200},
biburl =
{https://dblp.org/rec/journals/corr/abs-2108-
08516.bib},
bibsource = {dblp computer science bibliography,
https://dblp.org}
}

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.