The KITTI Vision Benchmark Suite

Method

Efficient 3D Deep LiDAR Odometry [EfficientLO-Net]
https://github.com/IRMVLab/EfficientLO-Net

Submitted on 3 Aug. 2022 16:10 by
Xinrui Wu (Shanghai Jiao Tong University)

Running time:		0.03 s
Environment:		1 core @ 2.5 Ghz (C/C++)

Method Description:

In this architecture, the projection-aware
representation of the 3D point cloud is proposed to
organize the raw 3D point cloud into an
ordered data form to achieve efficiency. The
Pyramid, Warping, and Cost volume (PWC) structure
for the LiDAR odometry task is built
to estimate and refine the pose in a coarse-to-fine
approach hierarchically and efficiently.

Parameters:

Refer to paper

Latex Bibtex:

@article{wang2021efficient,
title={Efficient 3D Deep LiDAR Odometry},
author={Wang, Guangming and Wu, Xinrui and Jiang,
Shuyang and Liu, Zhe and Wang, Hesheng},
journal={arXiv preprint arXiv:2111.02135},
year={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.