TaCOS: Task-Specific Camera Optimization with Simulation
arxiv(2024)
摘要
The performance of robots in their applications heavily depends on the
quality of sensory input. However, designing sensor payloads and their
parameters for specific robotic tasks is an expensive process that requires
well-established sensor knowledge and extensive experiments with physical
hardware. With cameras playing a pivotal role in robotic perception, we
introduce a novel end-to-end optimization approach for co-designing a camera
with specific robotic tasks by combining derivative-free and gradient-based
optimizers. The proposed method leverages recent computer graphics techniques
and physical camera characteristics to prototype the camera in software,
simulate operational environments and tasks for robots, and optimize the camera
design based on the desired tasks in a cost-effective way. We validate the
accuracy of our camera simulation by comparing it with physical cameras, and
demonstrate the design of cameras with stronger performance than common
off-the-shelf alternatives. Our approach supports the optimization of both
continuous and discrete camera parameters, manufacturing constraints, and can
be generalized to a broad range of camera design scenarios including multiple
cameras and unconventional cameras. This work advances the fully automated
design of cameras for specific robotics tasks.
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