The KITTI Vision Benchmark Suite

Method

Deep 3D LiDAR Odometry [D3DLO]

Submitted on 30 Nov. 2020 11:12 by
Philipp Adis (LfB at RWTH Aachen University)

Running time:		0.1 s
Environment:		GPU @ 2.5 Ghz (Python)

Method Description:

Raw LiDAR point clouds are sub-sampled to nc
remaining points and simultaneously features of
local regions are aggregated. Subsequently a
flow embedding is estimated between two
subsequent frames. The embeddings are further
sub-sampled and regressed to the transformation
parameters with a MLP.

Parameters:

\nc=1024

Latex Bibtex:

@misc{adis2021d3dlo,
title={D3DLO: Deep 3D LiDAR Odometry},
author={Philipp Adis and Nicolas Horst and
Mathias Wien},
year={2021},
eprint={2101.12242},
archivePrefix={arXiv},
primaryClass={cs.CV}
}

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.