Adaptive Imaging Technology of Robot Vision Sensor

This paper addresses the critical challenge of enhancing robotic real-time sensing and navigation capabilities in complex environments through advanced 3D modeling and semantic mapping technologies. The research integrates RGBD camera-based 3D modeling with synchronous positioning techniques to achieve precise environmental surface classification and monitoring. A novel approach combining multi-view recognition with improved segmentation quality is presented, along with a delay fusion method to address positioning errors in real-time visual-aided inertial navigation. The methodology demonstrates particular effectiveness in constructing semantic maps within dynamic complex environments, though success relies heavily on high-quality image processing and accurate synchronous positioning. Two experimental validations were conducted to investigate object perception and recognition mechanisms for robot-environment interaction. The study further explores adaptive imaging technology in robotic vision sensors and examines its future applications. This research contributes significantly to the field of autonomous robotics by enhancing environmental understanding and interaction capabilities, while acknowledging the technical constraints in real-world implementations. The findings suggest promising directions for improving robotic perception systems in complex operational scenarios.