The KITTI Vision Benchmark Suite

Method

Simple Mapping and Localization Estimation [SiMpLE]
https://github.com/vb44/SiMpLE

Submitted on 15 May. 2024 01:35 by
Vedant Bhandari (The University of Queensland)

Running time:		0.35 s
Environment:		>8 cores @ 2.5 Ghz (C/C++)

Method Description:

SiMpLE is a minimal configuration LiDAR odometry
method with only five configurable parameters.
SiMpLE is guided by maximising point cloud
coherence, fundamentally different from existing
error-based solutions.

Parameters:

sigma = 0.3 [m]
rMap = 0.5 [m]
rNew = 0.3 [m]
convergenceTolerance = 1e-06
minSensorRange = 0 [m]
maxSensorRange (hardware) = 120 [m]

Latex Bibtex:

@article{Bhandari2024,
author = {Vedant Bhandari and Tyson Govan
Phillips and Peter Ross McAree},
title ={Minimal configuration point cloud
odometry and mapping},
journal = {The International Journal of
Robotics Research},
volume = {0},
number = {0},
pages = {02783649241235325},
year = {0},
doi = {10.1177/02783649241235325}
}

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.