Thinning-based topological exploration using position probability of topological nodes
Tae‐Bum Kwon, Jae-Bok Song · 2006
Exploration is the fundamental task of guiding a robot autonomously during mapping so that it covers the entire environment with its sensors. In the frontier-based exploration, a robot visits the unknown regions, but the sufficient information on the obstacles was not exploited. In the topological exploration, the robot was forced to visit all the topological nodes, but it was inefficient and time-consuming. In this paper, an efficient exploration called a thinning-based topological exploration (TTE) is proposed. This scheme is based on the position probability of the end nodes of a topological map built in real time. The robot then updates the position probability of each end node sustaining its position at the current location using the range data. By analyzing this position probability, the robot can determine whether or not it needs to visit the specific end node to examine the environment around this node. Various experiments show that the proposed TTE algorithm can perform exploration more accurately than the frontier-based exploration approach and more efficiently than the other topological exploration schemes, because in most cases, exploration for the entire environment can be completed without directly visiting everywhere