Particle Filter based Outdoor Robot Localization using Natural Features Extracted from Laser Scanners

Martin Adams, Sen Zhang, Lihua Xie

Abstract: In this paper we present a new approach for natural feature extraction using a laser scanner for the purpose of localization in outdoor environments. In semi-structured outdoor environments, naturally predominant features such as trees and edges are considered. The proposed method applies a batch processing which carries out feature extraction after measurements from a full scan are received. The algorithm consists of data segmentation and parameter acquisition. A modified Gauss-Newton method is proposed for fitting circle parameters iteratively. The natural features extracted through this approach are more robust than those obtained by existing methods. In order to reduce the estimation error caused by the linearization in the extended Kalman filtering (EKF), a particle filter is applied to realize the prediction and validation by integrating data from both the laser range sensor and encoder in outdoor environments. The proposed feature extraction and localization algorithms are verified in a real world experiment.