Abstract
The objective of this article is to propose data processing from laser range finder that will provide simple, fast, and reliable object recognition including moving objects. The whole method is based on four steps: segmentation, simplification, correspondence between consequent measurements, and object classification. Segmentation uses raw data from laser range finder and it is significant in logical connection of related segments. The most important step is simplification which provides data reduction and acceleration of object classification. The output of simplification is an object represented by significant points. Correspondence between consequent measurements is based on kd-tree nearest neighborhood search. The object is then classified by its spatial changes. These changes are evaluated by position of correspondent significant points. Input of proposed procedure is a probabilistic model of laser range finder. In this article, versatile probabilistic model of Hokuyo URM-30 LX was used. The method was verified by simulations and by tests in real environment. The results show that proposed method is reliable and with small modifications (of parameters), it is usable with any other planar laser range finder.
Highlights
Common mobile robots work in environments, which are not separated from humans
The level of data degradation is negligible for the needs of mobile robotics
The significant points were used as object description for moving obstacle detection
Summary
Common mobile robots work in environments, which are not separated from humans. This means they need to be able to detect static parts of environments but they need to react to the moving obstacles, for example, different moving objects such as robots, humans, doors, and so on. The segmentation of data points from LRF measurements is the basic assumption of each algorithm used for the detection of moving obstacles. As it can be seen, algorithm compares angles between the line segments with the length greater than parameter mLen. In the case if the point li is too close to the previous point from simplified object, it will not be considered as significant point.
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