Text-MCL: Autonomous Mobile Robot Localization in Similar Environment Using Text-Level Semantic Information

Gengyu Ge,Qin Jiang,Wei Wang,Yi Zhang,Lihe Hu,Yang Wang

doi:10.3390/machines10030169

Abstract

Localization is one of the most important issues in mobile robotics, especially when an autonomous mobile robot performs a navigation task. The current and popular occupancy grid map, based on 2D LiDar simultaneous localization and mapping (SLAM), is suitable and easy for path planning, and the adaptive Monte Carlo localization (AMCL) method can realize localization in most of the rooms in indoor environments. However, the conventional method fails to locate the robot when there are similar and repeated geometric structures, like long corridors. To solve this problem, we present Text-MCL, a new method for robot localization based on text information and laser scan data. A coarse-to-fine localization paradigm is used for localization: firstly, we find the coarse place for global localization by finding text-level semantic information, and then get the fine local localization using the Monte Carlo localization (MCL) method based on laser data. Extensive experiments demonstrate that our approach improves the global localization speed and success rate to 96.2% with few particles. In addition, the mobile robot using our proposed approach can recover from robot kidnapping after a short movement, while conventional MCL methods converge to the wrong position.

Highlights

IntroductionAs a fundamental capability of an autonomous mobile robot, localization can assist the robot in knowing where it is
A puzzling problem is that the mobile robot using conventional adaptive Monte Carlo localization (AMCL) methods should be able to localize itself when it moves to either end of a corridor in theory because laser data from those areas have unique features different from other areas
The main contribution is the development of an integrated approach that combines text-level semantic information with a particle filter for both the global localization and robot kidnapping problem

Summary

Introduction

As a fundamental capability of an autonomous mobile robot, localization can assist the robot in knowing where it is. It is prior knowledge for path planning when the mobile robot performs a navigation task [1]. In an indoor environment where a global positioning system (GPS) signal is denied, the active perception method usually builds a map by employing SLAM techniques in advance [2,3], the mobile robot localizes itself according to the map. The commonly used solution is to use laser SLAM to build a 2D probability occupancy grid map [4]. The mobile robot can localize itself based on the current observations and previously built map. The most commonly used method is MCL, which is based on the particle filter algorithm [5], an implementation of the Bayes filter

Methods

Results

Conclusion