According to experts, Simultaneous Localization and Mapping (SLAM) is an intrinsic part of autonomous robotic systems. Several SLAM systems with impressive performance have been invented and used during the last several decades. However, there are still unresolved issues, such as how to deal with moving objects in dynamic situations. Classic SLAM systems depend on the assumption of a static environment, which becomes unworkable in highly dynamic situations. Several methods have been presented to tackle this issue in recent years, but each has its limitations. This research combines the visual SLAM systems ORB-SLAM3 and Detectron2 to present the Det-SLAM system, which employs depth information and semantic segmentation to identify and eradicate dynamic spots to accomplish semantic SLAM for dynamic situations. Evaluation of public TUM datasets indicates that Det-SLAM is more resilient than previous dynamic SLAM systems and can lower the estimated error of camera posture in dynamic indoor scenarios.
翻译:专家认为,同步本地化和绘图(SLAM)是自主机器人系统的一个固有部分,过去几十年中已经发明和使用了一些性能令人印象深刻的SLAM系统,然而,还存在一些未决问题,例如如何在动态情况下处理移动物体;典型的SLAM系统取决于一种静止环境的假设,这种环境在高度动态情况下变得不可行;近年来提出了解决这一问题的几种方法,但每种方法都有其局限性;这一研究结合了视觉SLM系统ORB-SLAM3和Setron2,提出了Det-SLAM系统,该系统利用深度信息和语义分解来查明和消除动态地点,以便在动态情况下完成SLAM的语义化点;对公共TUM数据集的评估表明,Det-SLAM系统比以往的动态SLM系统更具弹性,可以降低动态室内情景下摄影机姿势的估计错误。