This article discusses Panopticus, a system designed for omnidirectional 3D object detection using cameras on resource-constrained edge devices. It highlights the importance of this technology for safety-critical applications like mobile robot navigation. By utilizing an adaptive multi-branch detection scheme, Panopticus optimally adjusts its architecture based on available resources and spatial contexts, ultimately improving detection accuracy by 62% while maintaining a strict latency threshold of 33ms. The implementation on multiple edge devices showcases its effectiveness across real-world environments, emphasizing the transition from LiDAR to camera-based systems for cost efficiency.
Panopticus employs an adaptive multi-branch detection scheme that effectively navigates the challenges of edge devices, dynamically optimizing architecture based on resource availability and spatial characteristics.
Our experiments across varied environments demonstrate that Panopticus significantly enhances 3D object detection accuracy by an average of 62%, while adhering to a strict latency of 33ms.
This research addresses the computational challenges faced in deploying camera-based 3D detection systems on resource-constrained edge devices, particularly in critical applications like mobile robot navigation.
The adoption of cameras over LiDAR as a cost-effective solution for 3D detection is highlighted, yet the compute demands remain a central challenge that Panopticus aims to solve.
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