With the fast and unstoppable evolution of robotics and artificial intelligence, effective autonomous navigation in real-world scenarios has become one of the most pressing challenges in the literature. However, demanding requirements, such as real-time operation, energy and computational efficiency, robustness, and reliability, make most current solutions unsuitable for real-world challenges. Thus, researchers are forced to seek innovative approaches, such as bio-inspired solutions. Indeed, animals have the intrinsic ability to efficiently perceive, understand, and navigate their unstructured surroundings. To do so, they exploit self-motion cues, proprioception, and visual flow in a cognitive process to map their environment and locate themselves within it. Computational neuroscientists aim to answer ''how'' and ''why'' such cognitive processes occur in the brain, to design novel neuromorphic sensors and methods that imitate biological processing. This survey aims to comprehensively review the application of brain-inspired strategies to autonomous navigation, considering: neuromorphic perception and asynchronous event processing, energy-efficient and adaptive learning, or the imitation of the working principles of brain areas that play a crucial role in navigation such as the hippocampus or the entorhinal cortex.

翻译：随着机器人与人工智能领域的快速且不可阻挡的演进，在现实世界场景中实现有效的自主导航已成为文献中最紧迫的挑战之一。然而，诸如实时操作、能源与计算效率、鲁棒性以及可靠性等苛刻要求，使得当前大多数解决方案难以应对现实世界的挑战。因此，研究人员被迫寻求创新方法，例如仿生解决方案。事实上，动物天生具备高效感知、理解并导航其非结构化环境的内在能力。为此，它们利用自运动线索、本体感觉和视觉流，通过认知过程来映射环境并在其中定位自身。计算神经科学家旨在回答此类认知过程在大脑中"如何"以及"为何"发生，从而设计模仿生物处理的新型神经形态传感器与方法。本综述旨在全面回顾受大脑启发的策略在自主导航中的应用，涵盖：神经形态感知与异步事件处理、高能效与自适应学习，以及对导航中起关键作用的大脑区域（如海马体或内嗅皮层）工作原理的模仿。