Neuromorphic Computing for Real-Time Sensor Fusion in Autonomous Systems: Conceptual Framework and Potential Applications

Abstract

Neuromorphic computing, inspired by the structure and function of biological neural systems, offers transformative potential for real-time sensor fusion in resource-constrained autonomous unmanned vehicles. This paper presents a comprehensive conceptual framework for integrating neuromorphic processors—including spiking neural networks (SNNs), event-based cameras, and neuromorphic auditory sensors—into autonomous system architectures. We examine the fundamental principles distinguishing neuromorphic computing from conventional approaches: asynchronous event-driven processing, sparse coding, co-located memory and computation, and ultra-low power operation. The framework addresses the mapping of sensor fusion tasks—including visual-inertial odometry, multi-modal object detection, and dynamic environment modeling—onto neuromorphic substrates such as Intel’s Loihi, IBM’s TrueNorth, and SpiNNaker platforms. We analyze the inherent advantages of neuromorphic processing for handling asynchronous, multi-rate sensor streams from cameras, LiDAR, radar, IMUs, and acoustic arrays, demonstrating how temporal coding naturally accommodates varying sensor update frequencies. The conceptual architecture incorporates hierarchical SNN structures for feature extraction, attentional mechanisms for dynamic sensor prioritization, and predictive coding frameworks for robust state estimation under sensor degradation. Potential applications are explored across unmanned system domains: neuromorphic vision for high-speed UAV navigation in cluttered environments, event-based obstacle avoidance for micro-robots, and low-latency sensor fusion for autonomous underwater vehicles in turbid waters. We critically evaluate current limitations including the scarcity of training algorithms for complex SNNs, challenges in converting pre-trained deep networks to spiking equivalents, and the lack of standardized development tools. The paper examines emerging solutions including surrogate gradient methods, spike-timing-dependent plasticity variants, and hybrid architectures combining conventional and neuromorphic processing. Quantitative projections suggest potential energy efficiency improvements of 100-1000× compared to GPU-based implementations for specific sensor fusion tasks, enabling extended mission durations for battery-powered platforms.