Topological Map Generation for Intrinsic Visual Navigation of an Intelligent Service Robot

Abstract

Navigation methods nowadays are mainly based on metric maps, and a set of explicit geometric coordinates must be provided. However, a service robot may struggle in reaching this precise coordinate. If there is an obstacle preventing the robot from reaching the goal coordinate, the robot will judge that it has not reached the goal even if it has already reached the area such as the "kitchen". Topological maps are a good way to record semantic meanings of the environments, such as images. We integrate metric maps and topological maps together for a mobile robot to perform navigation. We propose an image-based particle filter which can generate a more flexible estimated robot pose. We test our proposed method in an 1000 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> indoor environment. The experimental result shows that our proposed method can reduce total navigation time and reach higher success rate in navigation stage. We show that our proposed method is suitable for intelligent service robots, and can be used in any indoor area with ease.