Predictive exploration considering previously mapped environments

The ability to explore an unknown environment is an important prerequisite for building truly autonomous robots. The central decision that a robot needs to make when exploring an unknown environment is to select the next view point(s) for gathering observations. In this paper, we consider the problem of how to select view points that support the underlying mapping process. We propose a novel approach that makes predictions about the structure of the environments in the unexplored areas by relying on maps acquired previously. Our approach seeks to find similarities between the current surroundings of the robot and previously acquired maps stored in a database in order to predict how the environment may expand in the unknown areas. This allows us to predict potential future loop closures early. This knowledge is used in the view point selection to actively close loops and in this way reduce the uncertainty in the robot's belief. We implemented and tested the proposed approach. The experiments indicate that our method improves the ability of a robot to explore challenging environments and improves the quality of the resulting maps.

Authors

• Daniel Perea Strom
• Fabrizio Nenci
• Cyrill Stachniss

Keywords

• Uncertainty
• Robot sensing systems
• Databases
• Approximation methods
• Mobile robots
• Unexplored Area
• Unknown Environment
• Loop Closure
• Unknown Area
• Path Length
• Target Location
• Prediction Approach
• Background Knowledge
• Exploratory Approach
• Vantage Point
• Local Map
• Mobile Robot
• Environment In Order
• Mapping System
• Uncertainty Reduction
• Environment Map
• Laser Ranging
• Suitable Approximation
• Underground Environment
• Micro Air Vehicles