  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.
