Autonomous homing based on laser-camera fusion system

Building maps of unknown environments is a critical factor for autonomous navigation and homing, and this problem is especially challenging in large-scale environments. Recently, sensor fusion systems such as combinations of cameras and laser sensors have become popular in the effort to ensure a general level of performance in this task. In this paper, we present a new homing method in a large-scale environment using a laser-camera fusion system. Instead of fusing data to form a single map builder, we adaptively select sensor data to handle environments which contain ambiguity. For autonomous homing, we propose a new mapping strategy for building a hybrid map and a return strategy for selecting the next target waypoints efficiently. The experimental results demonstrate that the proposed algorithm enables the autonomous homing of a robot in a large-scale indoor environments in real time.

I. INTRODUCTION

Let us consider the following scenarios. A scout robot enters a dangerous site instead of humans to inspect the site and returns to the base without human assistance. Or, a robot carrying a patient in a hospital returns to the patient's room after his/her family or a nurse drives the robot to the places the patient wishes to visit. Autonomous homing is an important function to accomplish such tasks. Many studies related to Simultaneous Localization and Mapping (SLAM) [1] [2] [3] [4] [5] or path planning [6] using various sensors have been conducted to determine the essential elements for autonomous homing.