The KITTI Vision Benchmark Suite

Method

Scan-line Independent Lidar Odometry via Multi-metric Linear Least Square [la] [MULLS]
https://github.com/YuePanEdward/MULLS

Submitted on 1 Nov. 2020 11:08 by
Yue Pan (ETH Zurich)

Running time:		0.08 s
Environment:		4 cores @ 2.2 Ghz (C/C++)

Method Description:

A scan-line independent lidar odometry using
only point cloud as input. (without loop closure)
1.Roughly classified geometric feature points
extraction using PCA.
2.Transformation estimation using Linear
Least Square based on multiple error metrics
(MULLS-ICP).
3.Multiple weighting functions such as residual,
intensity consistency, direction balance, etc for
boosting the accuracy and robustness.

Parameters:

TODO

Latex Bibtex:

@article{pan2021mulls,
title={MULLS: Versatile LiDAR SLAM via Multi-
metric Linear Least Square},
author={Pan, Yue and Xiao, Pengchuan and He, Yujie
and Shao, Zhenlei and Li, Zesong},
journal={IEEE International Conference on Robotics
and Automation (ICRA) 2021.},
}

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.