LOSS-SLAM：轻量级开集语义同步定位与地图构建
LOSS-SLAM: Lightweight Open-Set Semantic Simultaneous Localization and Mapping
AI总结：
本文提出 LOSS-SLAM，将对象识别、定位与编码同概率图模型紧耦合，以更低计算开销实现优于现有方法的轻量级开集语义 SLAM。
摘要：
使机器人能够从对象层面理解世界，是实现更高层级自主性的关键基础。视觉领域基础模型的成功，已经赋予系统分割并识别世界中几乎所有对象的能力。然而，利用这些对象来定位机器人并构建世界的开集语义地图，仍然是一个开放的研究问题。在本工作中，一个用于识别、定位和编码对象的系统与概率图模型紧密耦合，以执行开集语义同步定位与地图构建（SLAM）。所呈现的结果表明，与现有开集方法、闭集方法和几何方法相比，所提出的轻量级对象编码能够实现更准确的基于对象的 SLAM，同时其计算开销低于现有开集地图构建方法。
英文摘要：
Enabling robots to understand the world in terms of objects is a critical building block towards higher level autonomy. The success of foundation models in vision has created the ability to segment and identify nearly all objects in the world. However, utilizing such objects to localize the robot and build an open-set semantic map of the world remains an open research question. In this work, a system of identifying, localizing, and encoding objects is tightly coupled with probabilistic graphical models for performing open-set semantic simultaneous localization and mapping (SLAM). Results are presented demonstrating that the proposed lightweight object encoding can be used to perform more accurate object-based SLAM than existing open-set methods, closed-set methods, and geometric methods while incurring a lower computational overhead than existing open-set mapping methods.
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。
