A new approach to vision-based unsupervised learning of unexplored indoor environment for autonomous land vehicle navigation

Abstract

A vision-based approach to unsupervised learning of the indoor environment for autonomous land vehicle (ALV) navigation is proposed. The ALV may, without human's involvement, self-navigate systematically in an unexplored closed environment, collect the information of the environment features, and then build a top-view map of the environment for later planned navigation or other applications. The learning system consists of three subsystems: a feature location subsystem, a model management subsystem, and an environment exploration subsystem. The feature location subsystem processes input images, and calculates the locations of the local features and the ALV by model matching techniques. To facilitate feature collection, two laser markers are mounted on the vehicle which project laser light on the corridor walls to form easily detectable line and corner features. The model management subsystem attaches the local model into a global one by merging matched corner pairs as well as line segment pairs. The environment exploration subsystem guides the ALV to explore the entire navigation environment by using the information of the learned model and the current ALV location. The guidance scheme is based on the use of a pushdown transducer derived from automata theory. A prototype learning system was implemented on a real vehicle, and simulations and experimental results in real environments show the feasibility of the proposed approach.