doktorski rad
Global localization is the problem of determining the pose of a mobile robot under global uncertainty. The novel algorithm introduced here is based on the modified Multiple Modul (MM) Estimator and exploits a soft gating of the measurements (SGM) to reduce the computational requirements of presented approach. The localization approach uses a hybrid representation of the robot environment, topological with elements of metric. This localization algorithm consists of a position estimation and orientation estimation part. The position part is based on a x and y histogram scan matching procedure, where x and y histograms are extracted directly from local occupancy grid maps, using Probability Scalar Transform (PST). Histograms obtained at the actual mobile robot pose are compared to nodes histograms with activity status from gated volume to compute position correction. The orientation part is based on two proposed methods: Obstacle Vector Transform (OVT) combined with Polar Histograms and Hough Transform (HT) combined with a non-iterative algorithm for determination of end points and length of straight-line parts combined with Angle Histograms. Histograms obtained at the actual mobile robot pose are compared to histograms saved at previous mobile robot poses to compute orientation correction. The proposed methods for mobile robot orientation correction are compared and present a worth alternative to the use of magnetic compass, particularly in environments with magnetic noise. Sensors used for local occupancy grid generation are sonars but other exteroceptive sensors like a laser range finder can also be used. Experimental results with mobile robot Pioneer 2DX simulator and real robot show the capacity of this method.
mobile robot localization; histogram based scans matching; Probability Scalar Transform; Obstacle Vector Transform; Hough Transform; Multiple-hypothesis Tracking; soft gating; modify Multiple Modul (MM) Estimator
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