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Autonomous systems, particularly in the domain of robotics and unmanned vehicles, have seen substantial growth over the past few decades. Central to the autonomy of these systems is their ability to understand and navigate through their environment. Two technologies have been crucial in achieving this: Simultaneous Localization and Mapping (SLAM), and path planning algorithms.
While SLAM allows these systems to localize themselves within an unknown environment while concurrently mapping it (S. Wang, Wu, & Zhang, 2019), path planning algorithms help in determining the most efficient route from a starting point to a destination (Xuemin et al., 2018). However, the data used for SLAM and path planning often come from various sensors, each with their own strengths and limitations. For example, while LiDAR provides high-resolution distance measurements, it may struggle in foggy or dusty conditions. In contrast, radar can operate well in adverse weather but might not offer the same level of detail (Xu et al., 2022). This is where multi-sensor fusion plays a pivotal role.
Multi-sensor fusion involves the integration of data from different types of sensors to create a more robust, comprehensive, and accurate representation of the environment (D.J. Yeong, Velasco-Hernandez, et al., 2021). It allows for the pooling of sensor strengths while mitigating their individual limitations. Over the years, various multi-sensor fusion techniques, such as Kalman Filters, Particle Filters, and Bayesian Networks, have been developed to combine heterogeneous sensor data effectively (Narjes & Asghar, 2019).
The synergy of multi-sensor fusion and path planning within the SLAM framework opens up new avenues for enhanced navigation and safety. By fusing sensor data, SLAM algorithms can generate more accurate maps, and path planning algorithms can make more informed decisions. This is of paramount importance in dynamic and uncertain environments where real-time decision-making is critical (Y. Zhao et al., 2022).