Simultaneous localization and mapping, as a fundamental task in computer vision, has gained higher demands for performance in recent years due to the rapid development of autonomous driving and unmanned aerial vehicles. Traditional SLAM algorithms highly rely on basic geometry features such as points and lines, which are susceptible to environment. Conversely, higher-level object features offer richer information that is crucial for enhancing the overall performance of the framework. However, the effective utilization of object features necessitates careful consideration of various challenges, including complexity and process velocity. Given the advantages and disadvantages of both high-level object feature and low-level geometry features, it becomes essential to make informed choices within the SLAM framework. Taking these factors into account, this paper provides a thorough comparison between geometry features and object features, analyzes the current mainstream application methods of object features in SLAM frameworks, and presents a comprehensive overview of the main challenges involved in object-based SLAM.

翻译：同步定位与地图构建作为计算机视觉中的基础任务，近年来由于自动驾驶和无人驾驶飞行器的快速发展，对其性能提出了更高要求。传统SLAM算法高度依赖点、线等基本几何特征，而这些特征易受环境影响。相反，更高级别的物体特征提供了更丰富的信息，对于提升框架的整体性能至关重要。然而，有效利用物体特征需要仔细考虑各种挑战，包括复杂性和处理速度。鉴于高级物体特征与低级几何特征各自的优缺点，在SLAM框架中做出明智选择变得至关重要。综合考虑这些因素，本文对几何特征与物体特征进行了全面比较，分析了当前物体特征在SLAM框架中的主流应用方法，并系统总结了基于物体的SLAM所面临的主要挑战。