基于3D语义共视图的语义SLAM精确回环检测
Towards Accurate Loop Closure Detection in Semantic SLAM with 3D Semantic Covisibility Graphs
AI总结：
本文提出SmSLAM+LCD方法，集成到语义SLAM系统中，结合3D语义与低级特征信息进行精确回环检测，有效减少漂移并避免相似场景歧义。
摘要：
回环检测对于纠正未知环境中同步定位与建图（SLAM）累积的误差是必要的。然而，基于低级几何或图像特征的传统回环检测方法可能因无法区分相似场景而导致高度歧义，从而可能发生错误的回环检测。尽管一些文献考虑了语义2D图像信息来检测回环，但很少有工作将3D场景比较作为语义SLAM系统的组成部分。本文提出了一种名为SmSLAM+LCD的方法，集成到语义SLAM系统中，结合高级3D语义信息和低级特征信息，以进行精确的回环检测和有效的漂移减少。测试结果证明了该方法的有效性。
英文摘要：
Loop closure is necessary for correcting errors accumulated in simultaneous localization and mapping (SLAM) in unknown environments. However, conventional loop closure methods based on low-level geometric or image features may cause high ambiguity by not distinguishing similar scenarios. Thus, incorrect loop closures can occur. Though semantic 2D image information is considered in some literature to detect loop closures, there is little work that compares 3D scenes as an integral part of a semantic SLAM system. This paper introduces an approach, called SmSLAM+LCD, integrated into a semantic SLAM system to combine high-level 3D semantic information and low-level feature information to conduct accurate loop closure detection and effective drift reduction. The effectiveness of our approach is demonstrated in testing results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
