The correct classification of the surrounding terrain is an important ability of a mobile robot that drives in outdoor environments. Our robot uses a 3D LIDAR and a camera to classify terrain as either asphalt, cobblestones, grass, or gravel. We build on previous work where we modeled the terrain as a Conditional random field to account for spatial dependencies, which improved results substantially. We now show how to speed up the spatial classification by defining a new energy term for neighborhood relations. Moreover, we now also consider temporal dependencies as the robot moves. This not only further improves the results, but makes it possible to build local terrain maps of the environment. We describe how to efficiently integrate the classification results of each time step into the map in a probabilistic manner. By also detecting obstacles with the LIDAR, the robot can build combined terrain and elevation maps. We show that these maps can be used for semantic robot localization.
@inproceedings{laible2014iros,
author = {Stefan Laible and Andreas Zell},
title = {Building Local Terrain Maps Using Spatio-Temporal Classification
for Semantic Robot Localization},
booktitle = {Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International
Conference on},
year = {2014},
pages = {4591--4597},
address = {Chicago, IL},
month = {September},
organization = {IEEE},
publisher = {IEEE},
abstract = {The correct classification of the surrounding terrain is an important
ability of a mobile robot that drives in outdoor environments. Our
robot uses a 3D LIDAR and a camera to classify terrain as either
asphalt, cobblestones, grass, or gravel. We build on previous work
where we modeled the terrain as a Conditional random field to account
for spatial dependencies, which improved results substantially. We
now show how to speed up the spatial classification by defining a
new energy term for neighborhood relations. Moreover, we now also
consider temporal dependencies as the robot moves. This not only
further improves the results, but makes it possible to build local
terrain maps of the environment. We describe how to efficiently integrate
the classification results of each time step into the map in a probabilistic
manner. By also detecting obstacles with the LIDAR, the robot can
build combined terrain and elevation maps. We show that these maps
can be used for semantic robot localization.},
doi = {10.1109/IROS.2014.6943213},
pdf = {http://www.cogsys.cs.uni-tuebingen.de/publikationen/2014/laible2014iros.pdf},
}