The KITTI Vision Benchmark Suite

Method

Parameterized Semantic Features Based Lidar Odometry[la] [ PSF-LO]

Submitted on 3 Nov. 2020 07:18 by
Guibin Chen (AD-Lab)

Running time:		0.2s
Environment:		4 cores @ 3.2 GHz

Method Description:

Parameterized Semantic Features Based Lidar-only
Odometry, which aims at leveraging semantic
information of point cloud to improve the LO
performance. We use self-designed light weight
PSF(Parameterized Semantic Features) to refine
the GeF(Geometric Features) matching. Besides, we
design an efficient instance-level method to
accurately recognize and remove the dynamic
objects while retaining static ones in the
semantic point cloud, which are beneficial to
further improve the accuracy of LO.

Parameters:

The max distance is 0.1

Latex Bibtex:

@INPROCEEDINGS{9561554, author={Chen, Guibin and
Wang, Bosheng and Wang, Xiaoliang and Deng, Huanjun
and Wang, Bing and Zhang, Shuo}, booktitle={2021
IEEE International Conference on Robotics and
Automation (ICRA)}, title={PSF-LO: Parameterized
Semantic Features Based Lidar Odometry}, year=
{2021}, volume={}, number={}, pages={5056-5062},
doi={10.1109/ICRA48506.2021.9561554}}

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.