The KITTI Vision Benchmark Suite

Method

[la] Multi-layered Probabilistic Normal Distributions Transform Lidar Odometry [PNDT LO]

Submitted on 8 Jan. 2020 19:02 by
Hyunki Hong (Seoul National University )

Running time:		0.2 s
Environment:		8 cores @ 3.5 Ghz (C/C++)

Method Description:

Lidar odometry using improved NDT called
Probabilistic NDT (PNDT).

Parameters:

d_1=1
d_2=3
# Gaussian component=3000
coarsest layer resolution=4m
finest layer resolution=1m
max iteration=20

Latex Bibtex:

@inproceedings{Hong2017IROS,
title = {Probabilistic normal distributions
transform representation for accurate 3d point
cloud registration},
author = {Hong, Hyunki and Lee, Beom Hee},
booktitle = {IEEE/RSJ International Conference
on Intelligent Robots and Systems (IROS)},
month = {Oct},
year={2017},
address={Vancouver, Canada}
}

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.