Feature extraction for outdoor mobile robot navigation based on a modified Gauss–Newton optimization approach

This paper discusses the problem of feature detection for semi-structured outdoor environments such as campuses and parks using laser range sensors. In these environments, commonly encountered natural features that can be very useful for mobile robot navigation include edges (large discontinuity) and circles (e.g., trees, pillars). The term feature is used to denote objects which are “likely” to be detectable when the sensor is moved to new locations. Note that there has been no systematic approach for feature detection in outdoor environments. In this paper, we present an algorithm for feature detection. The algorithm consists of data segmentation and parameter acquisition. A modified Gauss–Newton method is proposed for fitting circle parameters iteratively. Experimental results show that the proposed algorithm is efficient in detecting features for semi-structured outdoor environments and is applicable to real time simultaneous localization and mapping.