Quantum-Assisted Adaptive Stereo Vision System (QASVS) for Autonomous Robotics

Abstract

The quantum enhanced stereo vision technique is thus a groundbreaking effort toward autonomous robotics, where autonomous robots have high precision 3D mapping through the application of quantum computational principles to solve the problem of classical stereo vision systems. However, real-time robotic navigation and object recognition is not allowed by such traditional methods due to depth estimation errors, noise, and occlusion issues. Based on that, this research offers a Quantum Assisted Adaptive Stereo Vision System (QASVS) that makes use of quantum superposition to enhance the depth estimation parallelism, entanglement for noise reduction, and a quantum optimized disparity computation model to increase the mapping accuracy. The system makes significant improvements in depth perception and computing speed at a real-time adaptive level, which could not be accomplished by the above-mentioned methods. To test the proposed framework, it is tested in a dynamic environment and found to be performing better in low light and complex terrains. These systems can be most crucial in autonomous drones, robotic surgical systems, industrial automation, and space exploration because precise depth perception is necessary. Processing speed, image quality, and environmental adaptability are improved by comparing to traditional stereo vision techniques. The study foresees quantum computing as a revolution for 3D vision for robotics that in turn will lead to the quantum enhanced machine perception. The next step will be building quantum photonic processors, as well as applying AI-driven depth refinement for more optimization.