A Fast RANSAC-Based Registration Algorithm for Accurate Localization in Unknown Environments using LIDAR Measurements

The problem of accurate localization using only measurements from a LIDAR sensor is analyzed in this paper. The sensor is rigidly fixed on a generic moving platform, which moves on a plane. Practical on-line applications of localization algorithms impose constraints on the execution time, problem that is addressed in this paper and compared with other existing solutions. Due to the nature of the sensor adopted, the localization algorithm is based on a fast and accurate registration algorithm, which is able to deal with noisy measurements, outliers and dynamic environments. The proposed solution relies on the RANSAC algorithm in combination with a Huber kernel in order to cope with typical nuisances in LIDAR measurements. The robust registration is successively used in combination with an extended Kalman filter to track the trajectory of the LIDAR over time, hence to solve the localization problem. Simulations and experimental results are reported to show the feasibility of the proposed approach.

I. INTRODUCTION

One of the main concerns for autonomous mobile robots is their ability to localize w.r.t. a fixed reference frame as they travel in an environment. In literature, the localization problem is related to the robot's position estimation in a mapped environment, problem also referred in literature as kidnapped robot problem ([1]), and to the maintenance of positions information over time. Since the localization problem is a sensor related estimation problem, we aim to solve it using only the information retrieved from a laser range finder, or LIght Detection And Ranging (LIDAR).