Towards a Perception and Sensor Fusion Architecture for a Robotic Airship

摘要

Robotic unmanned aerial vehicles have an enormous potential as observation and data-gathering platforms for a wide variety of applications. This paper discusses components of a perception architecture being developed for AURORA (Autonomous Unmanned Remote Monitoring Robotic Airship). The AURORA project focusses on the development of the technologies required for substantially autonomous unmanned aerial vehicles, and for robotic airships in particular. We describe our approach to spatial representation, which incorporates a Markov Random Field (MRF) model used for encoding spatial inferences obtained from sensor imagery. We present a dynamic approach to target recognition that uses a cycle of hypothesis formulation, experiment planning for hypothesis validation, experiment execution, and hypothesis evaluation to confirm or reject the classification of targets into object classes. We also discuss an approach to automatic hovering and landing using visual servoing techniques and interaction matrices, and present preliminary experimental results from our work.