Grid-centric perception is a crucial field for mobile robot perception and navigation. Nonetheless, grid-centric perception is less prevalent than object-centric perception for autonomous driving as autonomous vehicles need to accurately perceive highly dynamic, large-scale outdoor traffic scenarios and the complexity and computational costs of grid-centric perception are high. The rapid development of deep learning techniques and hardware gives fresh insights into the evolution of grid-centric perception and enables the deployment of many real-time algorithms. Current industrial and academic research demonstrates the great advantages of grid-centric perception, such as comprehensive fine-grained environmental representation, greater robustness to occlusion, more efficient sensor fusion, and safer planning policies. Given the lack of current surveys for this rapidly expanding field, we present a hierarchically-structured review of grid-centric perception for autonomous vehicles. We organize previous and current knowledge of occupancy grid techniques and provide a systematic in-depth analysis of algorithms in terms of three aspects: feature representation, data utility, and applications in autonomous driving systems. Lastly, we present a summary of the current research trend and provide some probable future outlooks.

翻译：以网格为中心的感知是移动机器人感知与导航的关键领域。然而，对于自动驾驶而言，由于自动驾驶车辆需要精确感知高度动态、大规模户外交通场景，且网格中心感知的复杂性和计算成本较高，其普及程度低于以目标为中心的感知。深度学习技术与硬件的快速发展为网格中心感知的演进提供了新思路，并使得多种实时算法得以部署。当前工业与学术研究展现了网格中心感知的巨大优势，例如全面细粒度的环境表征、更强的遮挡鲁棒性、更高效的传感器融合以及更安全的规划策略。鉴于该快速发展领域缺乏最新综述，我们提出了一篇面向自动驾驶车辆网格中心感知的分层结构综述。我们系统梳理了占据栅格技术的既有与现有知识，并从特征表示、数据效用及在自动驾驶系统中的应用三个维度对算法进行了深入分析。最后，我们总结了当前研究趋势并提出了若干可能的未来展望。