Abstract

The chapter 'Computer Vision Techniques for Accurate Navigation and Perception in Autonomous Systems' explores the pivotal role of advanced computer vision methods in enhancing the capabilities of autonomous technologies. Focusing on fundamental and cutting-edge techniques, this work provides a comprehensive overview of how image processing, feature extraction, object detection, and classification contribute to improved navigation and environmental perception. The chapter addresses the integration of multi-modal sensing systems, which combine visual data with inputs from sensors such as LIDAR and radar, to achieve robust and accurate environmental understanding. Key challenges such as scalability, real-time processing, and adaptation to varying environmental conditions are critically examined. The discussion extends to emerging trends in data augmentation and the impact of new technologies on system performance and deployment. This chapter offers valuable insights into the continuous evolution of autonomous systems and their reliance on sophisticated computer vision techniques for effective operation in diverse and dynamic settings.

Introduction

The advent of autonomous systems has marked a transformative shift in various sectors, including transportation, logistics, and industrial automation [1]. Central to the functionality of these systems was the role of computer vision, which enables machines to interpret and interact with their surroundings through visual data [2]. As autonomous vehicles, drones, and robotic platforms become more sophisticated, the reliance on advanced computer vision techniques for accurate navigation and perception was increasingly critical [3]. These technologies underpin the ability of autonomous systems to operate safely and efficiently in complex and dynamic environments [4-6].

Fundamental computer vision techniques such as image processing, feature extraction, and object detection form the bedrock of these systems [7,8]. Image processing involves enhancing visual data to improve the clarity and utility of information captured by sensors [9,10]. Feature extraction techniques, including methods like SIFT and Speeded-Up Robust Features (SURF), allow for the identification and analysis of distinctive elements within images [11]. Object detection and classification, powered by advanced algorithms such as CNNs, enable systems to recognize and categorize objects within their visual field, thereby facilitating informed decision-making [12,13].