Multi-Sensor Data Fusion Meets Edge Computing for Intelligent Surface Vehicles

Sensor fusion will undoubtedly remain a cornerstone of enhanced perception and decision-making in numerous intelligent systems. As we all know, each type of sensor has advantages and disadvantages. The data obtained from a single sensor is frequently incomplete and unreliable. The complementary information from multi-sensor improves the reliability and robustness of the system, particularly while working in challenging conditions. To improve the situation awareness ability for intelligent surface vehicles (ISVs) under complex navigational conditions, many efforts have been devoted to developing advanced multi-sensor data fusion methods. However, these methods often suffer from the high computational cost and high latency on compute-constrained platforms. It is thus necessary to develop intelligent edge computing frameworks to accelerate the data fusion methods, making real-time environmental perception, behavior decision, and navigation control for ISVs. In this work, we will introduce the various sensors used in ISVs and discuss their advantages and disadvantages. We will also present the key steps and typical methods for multi-sensor heterogeneous data fusion suitable for ISVs. Furthermore, we present a review and recent advances for model lightweight. These methods will help reduce model parameters and accelerate model computation, thereby facilitating more efficient model deployment in ISVs.