Review of multi-sensor fusion SLAM for mobile robot

Simultaneous localization and mapping (SLAM) is a significant challenge for autonomous mobile robotics, as it necessitates the reconstruction of an unfamiliar environment while simultaneously determining the robot's position using a map. The mobile robot employed a diverse range of sensors in the SLAM process to gather and interpret a map representation. Geometric model-based strategies have historically been employed for addressing SLAM problems. However, these techniques are prone to errors in tough environments, and single-sensor SLAM systems have degradation and adverse effects on localization and mapping outcomes in extreme environments characterized by high dynamics or coefficient characteristics. Recently, there has been a significant increase in the development of multi-sensor fusion SLAM techniques aimed at achieving more stable and resilient systems. This paper focuses on the development process and contemporary research conducted on multi-sensor fusion SLAM. This paper initially presents the overall structure of SLAM. It then provides a detailed explanation of the functions of front-end odometry, back-end optimization, loopback detection, and map building modules. Additionally, it provides a summary of the algorithms employed in SLAM. Furthermore, it describes and summarizes the classical representative open source algorithms based on the types of sensors to be combined. It also introduces commonly utilized public datasets, along with accuracy evaluation indexes and measurement tools. In conclusion, this paper presents a comprehensive examination of the development process, along with a summary of prominent research studies on the fusion of many sensors in SLAM.