The KITTI Vision Benchmark Suite

Method

Sensor Aware Lidar Odometry [la] [SALO]

Submitted on 7 Jul. 2019 11:43 by
Dmitri Kovalenko (Yandex LLC)

Running time:		0.6 s
Environment:		1 core @ 2.5 Ghz (C/C++)

Method Description:

A lidar odometry method, integrating into the computation the knowledge
about the physics of the sensor, is proposed. A model of measurement error
enables higher precision in estimation of the point normal covariance.
Adjacent laser beams are used in a outlier correspondence rejection
scheme.

Parameters:

\kMatchCloudSize = 1

Latex Bibtex:

@inproceedings{kovalenko2019salo,
title={Sensor Aware Lidar Odometry},
author={Kovalenko, Dmitri and Korobkin, Mikhail and Minin, Andrey},
booktitle={2019 European Conference on Mobile Robots (ECMR)},
pages={1--6},
year={2019},
organization={IEEE}
}

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.