The KITTI Vision Benchmark Suite

Method

KISS-ICP [la] [KISS-ICP]
https://github.com/PRBonn/kissicp

Submitted on 13 Sep. 2022 14:25 by
Ignacio Martin Vizzo (University of Bonn)

Running time:		0.05 s
Environment:		1 core @ 4.5 Ghz (Python/C++)

Method Description:

KISS-ICP: In Defense of Point-to-Point ICP –
Simple, Accurate, and Robust Registration If Done
the Right Way

Parameters:

Initial threshold = 2.0m
min. deviation threshold = 0.1m
num_points_in_voxels = 20
voxel_size = 1.0m
factor voxel size map merge = 0.5
factor voxel size registration = 1.5
ICP convergence criteria threshold = 10^-4

Latex Bibtex:

@article{vizzo2023ral,
author = {Vizzo, Ignacio and Guadagnino,
Tiziano and Mersch, Benedikt and Wiesmann, Louis
and Behley, Jens and Stachniss, Cyrill},
title = {{KISS-ICP: In Defense of Point-to-
Point ICP -- Simple, Accurate, and Robust
Registration If Done the Right Way}},
journal = {IEEE Robotics and Automation
Letters (RA-L)},
pages = {1-8},
doi = {10.1109/LRA.2023.3236571},
volume = {8},
number = {2},
year = {2023},
codeurl = {https://github.com/PRBonn/kiss-
icp},
}

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.