Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach
Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach
Collaborative Exploration of Unknown Environments with Teams of Mobile Robots
Revised Papers from the International Seminar on Advances in Plan-Based Control of Robotic Agents,
High resolution terrain mapping using low altitude aerial stereo imagery
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Global estimation in constrained environments
International Journal of Robotics Research
Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems
Journal of Field Robotics
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This paper develops active Simultaneous Localisation And Mapping (SLAM) trajectory control strategies for multiple cooperating Unmanned Aerial Vehicles (UAVs) for tasks such as surveillance and picture compilation in Global Positioning System (GPS)-denied environments. Each UAV in the team uses inertial sensor and terrain sensor information to simultaneously localise the UAV while building a point feature map of the surrounding terrain, where map information is shared between vehicles over a data fusion network. Multi-vehicle active SLAM control architectures are proposed that actively plan the trajectories and motions of each of the vehicles in the team based on maximising information in the localisation and mapping estimates. We demonstrate and compare an ideal, centralised architecture, where a central planning node chooses optimal actions for each UAV, and a coordinated, decentralised architecture, where UAVs make their own control decisions based on common shared map information. The different architectures involve varying degrees of complexity and optimality through differing communications and computational requirements. Results are presented using a three-UAV team in a six-degree of freedom multi-UAV simulator.