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One of the fundamental problems in mobile robotics is simultaneous localisation and mapping (SLAM), where one or more robots build a map and localise within the map at the same time. Many mobile robots have differential drive wheels with pneumatic tyres. These robots are dependent on odometry pose estimation when performing map building or localisation tasks. Currently a three parameter odometry estimation...
Accurate localisation of an indoor robot critically depends on the odometry calibration which varies with different types of floor surfaces. Motion control accuracy of robots can be improved by independently calibrating odometry parameters for each floor surface [1]. This paper presents a new robot floor classification system based on motor current measurements with compensation for variations in...
Floor sensors allow a mobile robot to segment the environment into useful regions with properties associated with the floor, such as odometry calibration, cleaning requirements and semantic map labelling. This paper describes an accurate floor colour sensor and presents experimental results to show its effectiveness at classifying different surfaces using a Support Vector Machine (SVM). The sensor...
The calibration of mobile robot odometry and sensor extrinsic parameters can significantly improve the accuracy of robot mapping and localisation. This paper reports on the simultaneous calibration of wheeled robot odometry and its onboard sensor extrinsic parameters. The calibration is achieved with the offline optimisation of map quality using a particle swarm optimisation algorithm. The approach...
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