A Comprehensive Study on Advanced Simultaneous Localization and Mapping (Slam) and Multi-Sensor Perception Fusion for Intelligent Autonomous Systems

Abstract

Simultaneous Localization and Mapping (SLAM) has emerged as one of the foundational technologies enabling intelligent autonomous systems, including mobile robots, drones, autonomous vehicles, and smart industrial platforms. With the increasing complexity of real-world environments, single-sensor SLAM approaches often struggle with robustness, accuracy, and dynamic scene interpretation. Perception fusion, integrating data from multiple complementary sensors such as LiDAR, cameras, IMUs, RADAR, and depth sensors, has become essential for building resilient SLAM pipelines capable of addressing challenges like occlusion, illumination variance, sensor noise, and fast motion. This paper presents a detailed study of SLAM fundamentals, modern perception-fusion frameworks, recent advancements, challenges, and the future scope. Emphasis is placed on algorithmic evolution, real-time processing, multi-sensor integration strategies, and the development of generalizable SLAM architectures for next-generation autonomous systems.

KEYWORDS: SLAM, Perception Fusion, Multi-Sensor Integration, Visual SLAM, LiDAR SLAM, IMU Fusion, Autonomous Systems, Robotics, Mapping, Localization