Topological Indoor Localization & Navigation for Autonomous Industrial Mobile Manipulator

Abstract

Autonomous Industrial Mobile Manipulators (AIMMs) combine the advantages of both mobile robots and industrial manipulators, therefore they have great mobility, flexibility and functionality. However, great challenges arise when AIMMs are expected to perform various tasks in unstructured or semi-structured environments. Since AIMMs are typically configured in the indoor environments, indoor localization & navigation is one of these challenges. A novel topological indoor localization & navigation method is proposed in this paper. Based on the depth information provided by a popular Kinect sensor, a progressive Bayesian classifier is developed to realize direct corridor type identification. Instead of extracting features from single observation, information from multi-observations are fused to achieve a more robust performance. With the ability of directly determining the corridor types, a topological map generation and loop closing method is proposed to build the environment map through autonomous exploration. By using the Markov Localization and trajectory planning method, the robot can localize itself and navigating freely in the indoor environment. Experiments are performed on a recently built AIMM system, and the results verify the effectiveness of the proposed methodology.