NDLI: Terrain classification with conditional random fields on fused 3D LIDAR and camera data

Content Provider	IEEE Xplore Digital Library
Author	Laible, S. Khan, Y.N. Zell, A.
Copyright Year	2013
Description	Author affiliation: Comput. Sci. Dept., Univ. of Tubingen, Tubingen, Germany (Laible, S.; Zell, A.) \|\| Dept. of Machine Learning & Robot., Univ. of Stuttgart, Stuttgart, Germany (Khan, Y.N.)
Abstract	For a mobile robot to navigate safely and efficiently in an outdoor environment, it has to recognize its surrounding terrain. Our robot is equipped with a low-resolution 3D LIDAR and a color camera. The data from both sensors are fused to classify the terrain in front of the robot. Therefore, the ground plane is divided into a grid and each cell is classified as either asphalt, cobblestones, grass or gravel. We use height and intensity features for the LIDAR data and Local ternary patterns for the image data. By additionally taking into account the context-sensitive nature of the terrain, the results can be improved significantly. We present a method based on Conditional Random Fields and compare it with a Markov Random Field based approach. We show that the Conditional Random Field model is better suited for our task. We achieve an average true positive rate of 94.2% for classifying the grid cells into the four terrain classes.
Sponsorship	IEEE Robot. Autom. Soc.
Starting Page	172
Ending Page	177
File Size	366907
Page Count	6
File Format	PDF
ISBN	9781479902637
DOI	10.1109/ECMR.2013.6698838
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-25
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Laser radar Three-dimensional displays Image color analysis Cameras Robot sensing systems
Content Type	Text
Resource Type	Article

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