The KITTI Vision Benchmark Suite

Method

Onion-LO [Onion-LO]
https://github.com/huashu996/Onion-LO

Submitted on 27 Aug. 2025 11:05 by
Cheng Xiaolong (SEU)

Running time:		0.04 s
Environment:		>8 cores @ 2.5 Ghz (C/C++)

Method Description:

Onion-LO demonstrates strong compatibility with
various LiDAR types and reliable operation across
diverse scenarios. This is facilitated by an onion-
like point cloud processing structure termed Onion
Ball.

Parameters:

voxel_size=2.0
exp_key_num=1000
resolution_h=0.2
resolution_h=0.42
onion_layer_thickness=5.0
onion_angle=3.0

Latex Bibtex:

@ARTICLE{11129609,
author={Cheng, Xiaolong and Geng, Keke and Liu,
Zhichao and Ma, Tianxiao and Sun, Ye},
journal={IEEE Robotics and Automation Letters},
title={Onion-LO: Why Does LiDAR Odometry Fail
Across Different LiDAR Types and Scenarios?},
year={2025},
volume={},
number={},
pages={1-8},
keywords={Laser radar;Point cloud
compression;Odometry;Optimization;Sensors;Iterativ
e methods;Feature extraction;Accuracy;Sensor
phenomena and
characterization;Robustness;SLAM;localization;LiDA
R odometry},
doi={10.1109/LRA.2025.3600143}}

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.