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Simultaneous Localization and Mapping of an Autonomous Mobile Robot with Kidnap and Automatic Recovery Capabilities
This research addresses the problem of the position and orientation (pose) recovery after the robot being kidnapped, based on Laser Range Finder (LRF) sensor.
By now the Monte Carlo Localization (MCL) has been introduced as a useful localization method. However the computational load of MCL is extremely large and not efficient at the initial few steps (global localization), which causes the localization process to take long computation time after the robot has been kidnapped and resets the particles. This paper provides a methodology to solve it by fusing MCL with Fast Library for Approximate Nearest Neighbors (FLANN) machine learning technique. We design a feature for LRF data called Geometric Structure Feature Histogram (GSFH).The feature GSFH encodes the LRF data to use it as the descriptor in FLANN. By building the database previously and FLANN searching technique, we filter out the most impossible area and reduce the computation load of MCL. Both in simulation and real autonomous mobile robot experiments show the effectiveness of our method.
SLAM developed by this project can be used in dynamic environment. This navigation system can be applied in services robot and tour guided robot.