Exploration and topological map building in unknown environments

This paper is on simultaneous exploration and topological map building in completely unknown environments. We propose an approach that consists of a continuous loop of local decision-making (where potential search directions are added to the current node) and global decision-making (where the robot moves along one of these directions and adds possibly a new node to the map ) using the previously proposed bubble space representation. As the robot switches between the two in a continual manner, it creates a coarse graph representation of the environment. There is a node for every explored location with a set of associated edges that either indicate potential search directions or connect to the other explored locations. The so-far constructed topological map contains all the required information in regards to deciding where to move with comparatively low memory requirements. Experimental results with an extensive real data set demonstrate that large terrains can be explored and mapped efficiently using this approach.

I. Introduction

This paper is on the exploration and topological map building of unknown environments. As such, the robot should explore (visit different places) and build its map incrementally at the same time with maximal coverage and minimal expenditure.