Design of a Formation Control System to Assist Human Operators in Flying a Swarm of Robotic Blimps

Formation control is essential for swarm robotics, enabling coordinated behavior in complex environments. In this paper, we introduce a novel formation control system for an indoor blimp swarm using a specialized leader-follower approach enhanced with a dynamic leader-switching mechanism. This strategy allows any blimp to take on the leader role, distributing maneuvering demands across the swarm and enhancing overall formation stability. Only the leader blimp is manually controlled by a human operator, while follower blimps use onboard monocular cameras and a laser altimeter for relative position and altitude estimation. A leader-switching scheme is proposed to assist the human operator to maintain stability of the swarm, especially when a sharp turn is performed. Experimental results confirm that the leader-switching mechanism effectively maintains stable formations and adapts to dynamic indoor environments while assisting human operator.

DäRF: Boosting Radiance Fields from Sparse Inputs with Monocular Depth Adaptation

Neural radiance fields (NeRF) shows powerful performance in novel view synthesis and 3D geometry reconstruction, but it suffers from critical performance degradation when the number of known viewpoints is drastically reduced. Existing works attempt to overcome this problem by employing external priors, but their success is limited to certain types of scenes or datasets. Employing monocular depth estimation (MDE) networks, pretrained on large-scale RGB-D datasets, with powerful generalization capability would be a key to solving this problem: however, using MDE in conjunction with NeRF comes with a new set of challenges due to various ambiguity problems exhibited by monocular depths. In this light, we propose a novel framework, dubbed D\"aRF, that achieves robust NeRF reconstruction with a handful of real-world images by combining the strengths of NeRF and monocular depth estimation through online complementary training. Our framework imposes the MDE network's powerful geometry prior to NeRF representation at both seen and unseen viewpoints to enhance its robustness and coherence. In addition, we overcome the ambiguity problems of monocular depths through patch-wise scale-shift fitting and geometry distillation, which adapts the MDE network to produce depths aligned accurately with NeRF geometry. Experiments show our framework achieves state-of-the-art results both quantitatively and qualitatively, demonstrating consistent and reliable performance in both indoor and outdoor real-world datasets. Project page is available at https://ku-cvlab.github.io/DaRF/.