Robust laser scan matching in dynamic environments

Abstract

This paper presents a robust laser scan matching algorithm in dynamic environments. Scan matching is thought to be an essential function for mapping and localization of mobile robots. Our method is based on the random sample and consensus (RANSAC) algorithm known for its good robust parameter estimation of the model parameters. Different from the existing scan matching methods for mobile robots, we only use the raw data of laser scanning without odometer information to find the transformation between two given laser data sets. Our method does not require any feature extraction and also need not initial estimation to reach global optimum. We demonstrate the practical usability of the proposed approach through Experiment.