A fast, accurate and robust method for self-localization in polygonal environments using laser range finders

Abstract

Self-localization is important in almost all robotic tasks. For playing an aesthetic and effective game of robotic soccer, self-localization is a necessary prerequisite. When we designed our robotic soccer team for participating in robotic soccer competitions, it turned out that all existing approaches did not meet our requirements of being fast, accurate, and robust. For this reason, we developed a new method, which is presented and analyzed in this paper. This method is one of the key components and is probably one of the explanations for the success of our team in national and international competitions. We present also experimental evidence that our method outperforms other self-localization methods in the RoboCup environment.

Collective decision-making based on social odometry

“…Optimal filters and map-like representations have been applied with remarkable success to robot localization (Simmons and Koenig 1995;Cassandra et al 1996;Burgard et al 1996Burgard et al , 1998Gutmann et al 1999Gutmann et al , 2001Chong and Kleeman 1997;Wang 1988). Most of these approaches are based on Markov localization methods that make use of dead-reckoning and absolute or relative measurements.…”

Self-localization in Large-Scale Environments for the Bremen Autonomous Wheelchair

“…Therefore, the matching is a three-step process in this case: first, the new penultimate corner is matched according to the rules described in Sect. 3.1 and the similarity measure just defined in (9). Second, the probabilities are propagated to the adjacent junctions as discussed in Sect.…”

“…Therefore, the matching process has to be carried out slightly different as long as no real route corner has been detected. The implementation of this requirement is straightforwardly achieved by a further extension to the similarity measure calculation previously shown in (3) and refined in (9). The equation that includes the "before the first corner" case looks as follows for the assumption that c n is a real corner:…”

“…They determine the robot's locomotion by matching consecutive laser-scans and deriving their mutual shift. Gutmann and Nebel [8,9] use direct correlations in their LineMatch algorithm, Mojaev and Zell [14] employ a grid map as "short term memory", and Röfer [18] accumulates histograms as basic data structure for the correlation process.…”

Self-localization in dynamic environments based on laser and vision data

“…Particularly, one would appreciate a robust, accurate and efficient selflocalization method which allows a global localization of the robot. In certain polygonal environments a laser based localization method is capable of combining all these properties by correlating observed lines with an a priori line model of the environment [5]. However, often line features can rather be detected by a vision system than by a laser range finder.…”