A new filtering method of laser scanner data for automated vehicle obstacle recognition in unstructured environment

Abstract

Filtering of laser scanner data is important for effective and fast environment recognition of autonomous vehicle in unstructured environment where many uncertainties caused by complex terrain and various obstacles lead to the invalid data existing in laser scanner data. Traditional filtering methods are easy to cause false noise detecting in the complex and changing environment, because not only the size of the filter window is predetermined but also noise is determined under only one condition. A new filtering method is proposed in this paper. In this method, the size of filter window is adaptively determined by boundary points marked in the process of removing the isolated points which divide apparent different points into different groups carrying on a simple pre-classification. Meanwhile, twice threshold comparison is used to judge whether the point is noise or not. Experimental results comparing this method with traditional methods in unstructured environment show that our method reduces the probability of false noise detecting, which brings about the protection of edge and detail information.