Building semantic grid maps for domestic robot navigation

Xianyu Qi,Mingbo Li,Yingpin Sun,Wei Wang,Mei Yuan,Lin Xue,Yuliang Wang

doi:10.1177/1729881419900066

Abstract

This article proposes a semantic grid mapping method for domestic robot navigation. Occupancy grid maps are sufficient for mobile robots to complete point-to-point navigation tasks in 2-D small-scale environments. However, when used in the real domestic scene, grid maps are lack of semantic information for end users to specify navigation tasks conveniently. Semantic grid maps, enhancing the occupancy grid map with the semantics of objects and rooms, endowing the robots with the capacity of robust navigation skills and human-friendly operation modes, are thus proposed to overcome this limitation. In our method, an object semantic grid map is built with low-cost sonar and binocular stereovision sensors by correctly fusing the occupancy grid map and object point clouds. Topological spaces of each object are defined to make robots autonomously select navigation destinations. Based on the domestic common sense of the relationship between rooms and objects, topological segmentation is used to get room semantics. Our method is evaluated in a real homelike environment, and the results show that the generated map is at a satisfactory precision and feasible for a domestic mobile robot to complete navigation tasks commanded in natural language with a high success rate.

Highlights

Occupancy grid map is one of the most important environment representation methods in mobile robotics
The robots can successfully complete point-to-point navigation tasks.[6]. When it comes to using the grid maps in the real domestic scene, they are lack of semantic information for end users to order the robots to complete navigation tasks conveniently
We focus on creating semantic grid maps for robot navigation in domestic environments

Summary

Introduction

Occupancy grid map is one of the most important environment representation methods in mobile robotics. If an end user asks a robot to navigate to the position of a bed, he/she should know where the bed is in the grid map and manually specify a free grid as the goal point. We focus on creating semantic grid maps for robot navigation in domestic environments. Our method enhances the occupancy grid map with semantic concepts of objects and rooms. In this way, the robots can utilize traditional robust navigation algorithms and make natural interactions with end users. The robots can utilize traditional robust navigation algorithms and make natural interactions with end users To build this map, we first create an occupancy grid map with sonar sensors.

Related work

Experimental results

Conclusion