An Improved VSLAM for Mobile Robot Localization in Corridor Environment

Abstract

Localization is a fundamental capability for an autonomous mobile robot, especially in the navigation process. The commonly used laser-based simultaneous localization and mapping (SLAM) method can build a grid map of the indoor environment and realize localization task. However, when a robot comes to a long corridor where there exists many geometrically symmetrical and similar structures, it often fails to position itself. Besides, the environment is not represented to a semantic level that the robot cannot interact well with users. To solve these crucial issues, in this paper, we propose an improved visual SLAM approach to realize a robust and precise global localization. The system is divided into two main steps. The first step is to construct a topological semantic map using visual SLAM, text detection and recognition, and laser sensor data. The second step is the localization which repeats part work of the first step but makes the best use of the prebuilt semantic map. Experiments show that our approach and solutions perform well and localize successfully almost everywhere in the corridor environment while traditional methods fail.