The KITTI Vision Benchmark Suite

Method

Semi-Direct Visual-LiDAR Odometry and Mapping [la] [SDV-LOAM]
https://github.com/ZikangYuan/SDV-LOAM

Submitted on 30 Nov. 2022 10:52 by
Zikang Yuan (HUST, School of Institute of Artificial Intelligence)

Running time:		0.06 s
Environment:		1 core @ 2.5 Ghz (C/C++)

Method Description:

A cascaded visual-LiDAR Odometry and Mapping
system. The LiDAR-assisted visual odometry
provides coarse pose estimation result and the
LiDAR odometry refines it.

Parameters:

Visual Module: 6 iters per frame
LiDAR Module: 5 iters per sweep

Latex Bibtex:

@article{10086694,
author={Yuan, Zikang and Wang, Qingjie and
Cheng, Ken and Hao, Tianyu and Yang, Xin},
journal={IEEE Transactions on Pattern Analysis
and Machine Intelligence},
title={SDV-LOAM: Semi-Direct Visual-LiDAR
Odometry and Mapping},
year={2023},
volume={},
number={},
pages={1-18},
doi={10.1109/TPAMI.2023.3262817}}

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.