Planning exploration strategies for simultaneous localization and mapping

In this paper, we present techniques that allow one or multiple mobile robots to efficiently explore and model their environment. While much existing research in the area of Simultaneous Localization and Mapping (SLAM) focuses on issues related to uncertainty in sensor data, our work focuses on the problem of planning optimal exploration strategies. We develop a utility function that measures the quality of proposed sensing locations, give a randomized algorithm for selecting an optimal next sensing location, and provide methods for extracting features from sensor data and merging these into an incrementally constructed map. We have also provide an efficient algorithm driven by our utility function. This algorithm is able to explore several steps ahead without incurring too high a computational cost. We have compared that exploration strategy with a totally greedy algorithm that optimizes our utility function with a one-step-look ahead. The planning algorithms which have been develope...

• Type: Journal
• Year: 2006
• Where: RAS
• Authors: Benjamín Tovar, Lourdes Muñoz-Gómez, Rafael Murrieta-Cid, Moises Alencastre-Miranda, Raul Monroy, Seth Hutchinson
• Keywords: Algorithms, RAS 2006, Sensors, Utility Function