Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans
Journal of Intelligent and Robotic Systems
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SLAM is a method of map building and self-position estimation for robot navigation. However, map building error is especially appeared in loop closing points when the mobile robot moves around loop trajectories. In this study, more accurate mobile robot SLAM is considered in intelligent space [1] where many sensors are distributed. An intelligent space is constructed with various types of distributed sensors including networked laser range sensors. Laser sensor on a mobile robot and environment sensors share sensor information each other in intelligent space. Maps and self-positions of the mobile robot are estimated using geometrical relationships between the mobile robot and sensors in intelligent space. However, geometrical calibration of distributed sensors under the unified world coordinates is required for construction of the intelligent space. When many sensors are distributed in wide area, it generally becomes complicated tasks to calibrate all sensors. In order to solve these problems, we consider extend SLAM algorithm. In this study, a new method of SLAM, which uses distributed sensors fixed in the intelligent space, is introduced. This method aims to achieve precision SLAM and position estimation of networked laser range sensors in the intelligent space.