The KITTI Vision Benchmark Suite

Method

Surfel-based Mapping [la] [lc] [SuMa]

Submitted on 25 May. 2018 00:29 by
Jens Behley (University of Bonn)

Running time:		0.1 s
Environment:		1 core @ 3.5 Ghz (C/C++)

Method Description:

Laser-based method that uses a surfel mapping approach to represent current and past laser point clouds and projective ICP to find correspondences in frame-to-model tracking. The method uses OpenGL to attain real-time laser-based odometry.

Parameters:

see paper.

Latex Bibtex:

@inproceedings{behley2018rss,
author = {J. Behley and C. Stachniss},
title = {Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments},
booktitle = {Robotics: Science and Systems (RSS)},
year = 2018,
}

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.