The KITTI Vision Benchmark Suite

Method

Efficient LiDAR Odometry [la] [ELO]

Submitted on 2 Mar. 2021 17:20 by
Xin Zheng (Zhejiang University)

Running time:		0.005 s
Environment:		GPU @ 2.6 Ghz (C/C++)(0.027s Jetson AGX)

Method Description:

A novel efficient LiDAR odometry approach by taking
advantage of both non-ground spherical range image
and bird's-eye-view map for ground points.

Parameters:

Spherical range image 2048x80.
Bird's-eye-view map 120m x 60m.

Latex Bibtex:

@ARTICLE{zheng2021efficient,
author={Zheng, Xin and Zhu, Jianke},
journal={IEEE Robotics and Automation Letters(RA-
L)},
title={Efficient LiDAR Odometry for Autonomous
Driving},
year={2021},
volume={6},
number={4},
pages={8458-8465},
doi={10.1109/LRA.2021.3110372}}

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.