The KITTI Vision Benchmark Suite

Method

Real-Time Inertial Lidar Odometry with Two-Scan Motion Compensation [la] [MC2SLAM]

Submitted on 30 May. 2018 14:56 by
Frank Neuhaus (University of Koblenz-Landau)

Running time:		0.1 s
Environment:		4 cores @ 2.5 Ghz (C/C++)

Method Description:

Odometry and SLAM algorithm that uses only lidar
data. Velodyne scans are simultaneously motion
compensated and matched against a local map.

Parameters:

As specified in the paper.

Latex Bibtex:

@inproceedings{NeuhausGCPR2018,
author = {Neuhaus, Frank and Koss, Tilmann
and Kohnen, Robert and Paulus, Dietrich},
booktitle = {German Conference on Pattern
Recognition},
title = {MC2SLAM: Real-Time Inertial Lidar
Odometry
using Two-Scan Motion Compensation},
year = {2018},
location = {Stuttgart},
organization={Springer},
note={To appear},
}

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.