Multi-Scale Signal Processing in Image-Processing-Based Robotics: Enhancing Visual Perception and Control

Abstract

Multi-scale signal processing has emerged as a fundamental strategy in image-processing-based robotics, offering significant improvements in visual perception, environmental understanding, and task execution. This paper explores foundational methodologies such as wavelet transforms and image pyramids, and their critical role in robot autonomy, including SLAM, object detection, and manipulation in complex settings. Furthermore, it addresses the real-time deployment of these methods through embedded processing and sensor fusion, integrating machine learning for adaptive perception. The findings underscore that multi-scale frameworks are not only computationally effective but also essential in enabling intelligent and scalable robotic systems.