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Formation-based 3D Mapping of Micro Aerial Vehicles

Mark Lester F. Padilla, Pakpong Chirarattananon, Argel A. Bandala, Ryan Rhay P. Vicerra, Renann G. Baldovino, Elmer P. Dadios

Year
2019
Citations
2

Abstract

Micro Aerial Vehicles have brought tremendous interests to the research community, particularly in localization and mapping. While there are many commercially available sensors, such as Laser Range Finders (LRF) and RGBD cameras, that provide accurate 3D maps, they usually have significant power and payload requirements. This means, small flying robots are unable to handle such sensors. This study explores the possibility of collaborative mapping using formations from multiple simple cameras to obtain an accurate map similar to that of the LRF and RGBD cameras. By using multiple small robots and integrating them as one, we have created a platform for 3D reconstruction in which formations can be incorporated. Thus, the proposed method can be used with a low-cost system for surveying, disaster management, and surveillance in the future.

Keywords

Payload (computing)Computer scienceRobotComputer visionArtificial intelligenceSimultaneous localization and mappingRange (aeronautics)Real-time computingMobile robotEngineering

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