The KITTI Vision Benchmark Suite

Method

Intensity Scan Context based Lidar Odometry And Mapping [la] [lc] [ISC-LOAM]
https://github.com/wh200720041/iscloam

Submitted on 11 Mar. 2021 13:23 by
Han Wang (Nanyang Technological University)

Running time:		0.1 s
Environment:		4 cores @ 3.0 Ghz (C/C++)

Method Description:

A full slam implementation, with intensity scan
context loop closure detection at back-end and fast
lidar odometry and mapping at front-end.

Parameters:

\geometry_threshold=0.67
\intensity_threshold=0.93

Latex Bibtex:

@inproceedings{wang2020intensity,
title={Intensity scan context: Coding intensity
and geometry relations for loop closure detection},
author={Wang, Han and Wang, Chen and Xie, Lihua},
booktitle={2020 IEEE International Conference on
Robotics and Automation (ICRA)},
pages={2095--2101},
year={2020},
organization={IEEE}
}

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.