Karsten Bohlmann and Peter Biber and Andreas Zell

Using Geographical Data and Sonar to Improve GPS Localization for Mobile Robots

4th European Conference on Mobile Robots (ECMR 2009), Mlini/Dubrovnik, Croatia, 2009, pp. 55-60


Abstract

Internet technology and the availability of large public knowledge bases should enable future autonomous systems to drastically improve their perceptual and cognitive capabilities with only inexpensive sensors. In this paper we investigate this aspect with respect to robot self-localization. We present a method to improve GPS-based localization of mobile robots using geographic data from a public database. From a cadastral map a basic map of a robot's working area is automatically created. A mobile robot is equipped with a low-cost GPS receiver and ultrasonic sensors. Then, a particle filter is used to fuse GPS position values and odometry data and to match sonar scan data with the a priori geodata map. The map is also updated with previously unknown environment features. The algorithm was tested in an outdoor environment with uneven terrain. Experimental results show considerable improvements in position estimation compared to using GPS alone.


Downloads and Links

[pdf]


BibTeX

@inproceedings{bohlmann2009ecmr,
  author = {Karsten Bohlmann and Peter Biber and Andreas Zell},
  title = { Using Geographical Data and Sonar to Improve {GPS} Localization
	for Mobile Robots},
  booktitle = {4th European Conference on Mobile Robots (ECMR 2009)},
  year = {2009},
  pages = {55--60},
  address = {Mlini/Dubrovnik, Croatia},
  month = sep,
  abstract = {Internet technology and the availability of large public knowledge
	bases should enable future autonomous systems to drastically improve
	their perceptual and cognitive capabilities with only inexpensive
	sensors. In this paper we investigate this aspect with respect to
	robot self-localization. We present a method to improve GPS-based
	localization of mobile robots using geographic data from a public
	database. From a cadastral map a basic map of a robot's working area
	is automatically created. A mobile robot is equipped with a low-cost
	GPS receiver and ultrasonic sensors. Then, a particle filter is used
	to fuse GPS position values and odometry data and to match sonar
	scan data with the a priori geodata map. The map is also updated
	with previously unknown environment features. The algorithm was tested
	in an outdoor environment with uneven terrain. Experimental results
	show considerable improvements in position estimation compared to
	using GPS alone. },
  affiliation = {Chair of Cognitive Systems, University of Tuebingen, Department of
	Computer Science, Sand 1, 72076 Tuebingen, Germany},
  url = {http://www.cogsys.cs.uni-tuebingen.de/publikationen/2009/bohlmann2009ecmr-geographical-data.pdf}
}