Monte Carlo localization in outdoor terrains using multilevel surface maps

We propose a novel combination of techniques for robustly estimating the position of a mobile robot in outdoor environments using range data. Our approach applies a particle filter to estimate the full six-dimensional state of the robot and utilizes multilevel surface maps which, in contrast to standard elevation maps, allow the robot to represent vertical structures and multiple levels in the environment. We describe probabilistic motion and sensor models to calculate the proposal distribution and to evaluate the likelihood of observations. We furthermore describe an active localization approach which actively selects the sensor orientation of the 2D laser range scanner to improve the localization results. To efficiently calculate the appropriate orientation we apply a clustering operation on the particles and only evaluate potential orientations based on these clusters. Experimental results obtained with a mobile robot in large-scale outdoor environments indicate that our approach y...

• Type: Journal
• Year: 2008
• Where: JFR
• Authors: Rainer Kümmerle, Rudolph Triebel, Patrick Pfaff, Wolfram Burgard
• Keywords: JFR 2008, Mobile Robot, Multilevel Surface, Outdoor Environments