Improving Area Center Robot Navigation Using a Novel Range Scan Segmentation Method
When using raw 2D range measures to delimit the border for the free area sensed by a robot, the noise makes the sensor to yield a cloud of points, which is an imprecise border. This vagueness pose some problems for robot navigation using area center methods, due to free area split points locations. The basic method, when locating split points, does not take into account environmental features, only the raw cloud of points. In order to determine accurately such environmental features we use a novel range scan segmentation method. This method has the interesting characteristic of being adaptive to environment noise, in the sense that we do not need to fix noise standard deviation, even different areas of the same scan can have different deviations, e. g. a wall besides a hedge. Procedure execution time is in the order of milliseconds for modern processors. Information about interesting navigational features is used to improve area center navigation by means of determining safer split points and developing the idea of dynamic split point. A dynamic split point change its position to a new feature if this new feature is considered more dangerous than the one marked by the split point.
KeywordsExtend Kalman Filter Outlier Detection Robot Navigation Split Point Line Extraction
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