Abstract

The advancement of autonomous systems relies heavily on sophisticated data collection and preprocessing techniques to achieve reliable and accurate environmental perception. This chapter provides a comprehensive exploration of advanced methodologies in data collection and preprocessing, focusing on the pivotal role of multi-sensor setups in enhancing perception accuracy. Emphasis was placed on the integration of various sensor modalities, including LiDAR, radar, cameras, and Inertial Measurement Units (IMUs), to address the limitations of individual sensors through data fusion techniques. The chapter delves into cutting-edge processing techniques for LiDAR and radar data, such as pulse compression, Doppler processing, and noise reduction, as well as advanced image preprocessing methods including edge detection and segmentation. Furthermore, the synergy between IMUs and other sensors was analyzed, highlighting how fused data improves motion estimation and spatial awareness. Through a detailed examination of these methodologies, the chapter aims to provide insights into optimizing autonomous system performance and adaptability in complex environments. Key topics include sensor fusion, data preprocessing, edge detection, motion sensing, radar processing, and LiDAR enhancement. This discussion contributes valuable knowledge to the field of autonomous systems, supporting advancements in technology and application.

Introduction

In the realm of autonomous systems, the efficacy of environmental perception was critically dependent on advanced data collection and preprocessing techniques [1]. As autonomous technologies become increasingly sophisticated, the need for robust methods to gather and refine sensor data has grown paramount [2]. The integration of various sensor modalities such as LiDAR, radar, cameras, and IMUs enables these systems to overcome the limitations associated with individual sensors [3,4]. Each sensor type offers unique strengths, but also has inherent weaknesses that can impact the quality of the data provide [5]. Thus, leveraging multiple sensors and employing advanced data fusion techniques are essential to achieving accurate and reliable perception capabilities [6].

Multi-sensor setups play a crucial role in enhancing the perception of autonomous systems by providing a more comprehensive view of the environment [7]. LiDAR systems offer precise three-dimensional mapping butstruggle with detecting objects under adverse conditions [8-10]. Radar complements this by detecting objects regardless of weather conditions, though with lower resolution [11]. Cameras provide rich visual data that enhances object recognition but can be affected by varying lighting conditions [12]. By combining data from these sensors, autonomous systems can achieve a more holistic and accurate representation of their surroundings, thereby improving their operational effectiveness and safety [13,14].