The KITTI Vision Benchmark Suite

Method

SuMa++: Efficient LiDAR-based Semantic SLAM [la] [lc] [SuMa++]
https://github.com/PRBonn/semantic_suma

Submitted on 26 Feb. 2019 14:34 by
Xieyuanli Chen (University of Bonn)

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

Method Description:

Our approach exploits ideas from a modern LiDAR
SLAM pipeline Surfel-based Mapping (SuMa) and
incorporates semantic information from a semantic
segmentation generated by a Fully Convolutional
Neural Network (FCN). This allows us to generate
high-quality semantic maps, while at the same time
improve the geometry of the map and the quality of
the odometry.

Parameters:

see paper.

Latex Bibtex:

@inproceedings{chen2019iros,
author = {X. Chen and A. Milioto and E. Palazzolo
and P. Gigu\`{e}re and J. Behley and C.
Stachniss},
title = {SuMa++: Efficient LiDAR-based Semantic
SLAM},
booktitle = {IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS)},
year = 2019,
codeurl =
{https://github.com/PRBonn/semantic_suma/},
videourl = {https://youtu.be/uo3ZuLuFAzk},
}

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.