A layered architecture for office delivery robots
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Experiences with a mobile robotic guide for the elderly
Eighteenth national conference on Artificial intelligence
Markov localization using correlation
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
An efficient area-based observation model for Monte-Carlo robot localization
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
How the Location of the Range Sensor Affects EKF-based Localization
Journal of Intelligent and Robotic Systems
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A main difficulty that arises in the context of probabilistic localization is the design of an appropriate observation model, i.e., determining the likelihood of a sensor measurement given the pose of the robot and a map of the environment. Many successful approaches to localization rely on data provided by range sensors, e.g., laser range scanners. When using such data one normally has to deal with erroneous maximum-range readings that occur due to poor-reflecting surfaces. In general, these readings cannot be distinguished from readings obtained when no obstacle is within the measurement range of the sensor. Therefore, existing localization techniques treat these readings alike in the observation model. In this paper, we present a novel approach that explicitly considers the reflection properties of surfaces and thus the expectation of valid range measurements. In addition to the expected range measurement, we compute the probability of reflectance for a beam given the relative pose of the robot to the obstacle taking into account the angle of incidence of the beam. We estimate the reflection properties of surfaces using data collected with a mobile robot equipped with a laser range scanner. As we demonstrate in experiments carried out with a real robot, our technique leads to significantly improved localization results compared to a state-of-the-art observation model.