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Multi-view approach for drone light show

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Abstract

Drones, or precisely quadrotors, have been increasingly used in the field of robotics and also in entertainment. Coordinated multiple drones that form visual presentations through their equipped LEDs are known as drone light shows (Waibel, M., Keays, B., Augugliaro, F.: in Drone shows: Creative potential and best practices. ETH Zurich, 2017). Such a performance offers visual enjoyment for a large audience, particularly in festivals. However, the majority of current drone light shows are manually coordinated by personnel using software. Drone light shows also have limited viewing range, thereby preventing the audience from getting a good view of the actual performance. This study proposes a method to provide multiple visual presentations in accordance with multiple viewing angles. We use visual hull to filter out the candidate areas that form input images, and takes projection error and classification values as weight for optimization. Consequently, the proposed method reduces the number of drones needed to form a multi-view structure for visual presentations. Furthermore, to meet the demands of performing the animation in multi-view structure, we implement the flight algorithm to locate the most suitable corresponding points between two different structures, and then generate the shortest flight paths without collision. Experiments conducted in our simulator provide additional insights and discussions, and each factor is visualized to provide an improved understanding of our approach for multi-view drone light shows.

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Acknowledgements

This work was supported by The Ministry of Science and Technology, Taiwan, under GRANT No. MOST 110-2221-E-004-009 and MOST 110-2634-F-004-001 through Pervasive Artificial Intelligence Research (PAIR) Labs. We would also thank Sin-Fei Lee for the demo video editing.

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  1. Department of Computer Science, National Chengchi University, Taipei, Taiwan

    Kai-Chun Weng, Shu-Ting Lin, Chen-Chi Hu, Ru-Tai Soong & Ming-Te Chi

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  1. Kai-Chun Weng
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Correspondence to Ming-Te Chi.

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Weng, KC., Lin, ST., Hu, CC. et al. Multi-view approach for drone light show. Vis Comput 39, 5797–5808 (2023). https://doi.org/10.1007/s00371-022-02696-8

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