The KITTI Vision Benchmark Suite

Method

LiDAR SLAM based on Implicit Moving Least Squares Surfaces [la] [IMLS-SLAM]

Submitted on 6 Sep. 2017 12:28 by
Jean-Emmanuel Deschaud (MINES ParisTech)

Running time:		1.25 s
Environment:		1 core @ >3.5 Ghz (C/C++)

Method Description:

SLAM based on Velodyne data doing ICP Point to
Model (defined as Point Set surfaces) and taking
specific stable sampling.

Parameters:

The main parameter is the number n of previous
sweeps of the Velodyne kept as a map

Latex Bibtex:

@INPROCEEDINGS{8460653,
author={J. {Deschaud}},
booktitle={2018 IEEE International Conference on
Robotics and Automation (ICRA)},
title={IMLS-SLAM: Scan-to-Model Matching Based
on 3D Data},
year={2018},
volume={},
number={},
pages={2480-2485},}

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.