Moving obstacles detection based on laser range finder measurements

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.