Enhancing object detection and localization through multi-sensor fusion for smart city infrastructure

Abstract

The rapid advancement in autonomous systems and smart city infrastructure demands sophisticated object detection and localization capabilities to ensure safety, efficiency, and reliability. Traditional single sensor approaches often fall short, especially under complex environmental conditions. This paper introduces the CLR-Localiser, a novel multi-sensor fusion framework that synergistically integrates data from cameras, LiDAR and radar sensors mounted on roadside infrastructure to enhance object detection and 3D localization. Leveraging the complementary strengths of each sensor type, the CLR-Localiser employs an early fusion approach and deep learning techniques, including convolutional neural networks for object detection and regression networks for precise localization. We rigorously validated the performance of the CLR-Localiser against the benchmark Kitti dataset, and a custom dataset specifically designed for this research, demonstrating significant improvements in detection accuracy, localization precision, and object-tracking capabilities under diverse conditions. Our findings highlight the CLR-Localiser's potential to overcome the limitations of conventional monocular and single-sensor methods, offering a robust solution for autonomous driving, robotics, surveillance, and industrial automation applications. The development and validation of the CLR-Localiser not only prove the technical feasibility of early sensor data fusion but also pave the way for future advancements in multi-sensor fusion technology for enhanced environmental perception in autonomous systems.