Multi-Scale Signal Processing in Image-Processing-Based Robotics

Abstract

The integration of multi-scale signal processing techniques in image-processing-based robotics has significantly enhanced the performance, adaptability, and intelligence of autonomous robotic systems. By exploiting hierarchical structures in image signals, these techniques enable robots to perceive and interpret visual information across varying spatial and temporal resolutions. This article explores the theoretical foundation and practical implementation of multi-scale approaches—including pyramid decomposition, wavelet transforms, and scale-invariant feature extraction—in robotics. Applications span object recognition, navigation, manipulation, and real-time decision-making. The article also highlights current challenges and future research directions in harmonizing multi-scale methods with learning-based perception modules in robotics.