发表机构
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology（计算机科学与人工智能实验室，麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对 SLAM 中数据关联难题，提出广义无数据关联 SLAM 框架，利用深度学习语义信息，通过半增量估计方案联合估计多要素，经合成与真实数据集评估，相比基线展现出卓越性能，提升框架实用性与可解释性。
AI 中文摘要
地标测量与地标变量之间的数据关联一直是 SLAM 的核心挑战，因为估计精度关键取决于将测量与正确的地标变量相关联。深度学习的进展为该问题带来新机遇，数据关联现在不仅可以利用位置测量，还可以利用关于物体地标的语义信息。本文提出了一个广义的无数据关联 SLAM 框架，该框架从里程计以及地标的位置和语义测量中联合估计数据关联、机器人位姿、地标位置和地标语义。所提出的框架在数据关联和地标语义估计之间创造协同作用，采用半增量估计方案提高准确性和计算效率，并为地标数量估计提供原则性依据、指导方针和启发式方法，提高框架的可解释性和实际可用性。所提出的框架和算法在具有两种语义信息（类别标签和实值特征向量）的合成和真实世界数据集上进行了评估，与强大的基线相比表现出卓越性能。
英文摘要
Data association between landmark measurements and landmark variables has long been a central challenge in SLAM, as estimation accuracy depends critically on associating measurements with the correct landmark variables. Recent advances in deep learning have created new opportunities for the problem; data association can now leverage not only positional measurements but also semantic information about object landmarks, such as class labels from neural object detectors and feature vectors from visual foundation models. In this paper, we present a generalized data-association-free SLAM framework that jointly estimates data associations, robot poses, landmark positions, and landmark semantics from odometry, and positional and semantic measurements of landmarks. The proposed framework (i) creates a synergy between data association and landmark semantics estimation; (ii) adopts a semi-incremental estimation scheme for improved accuracy and computational efficiency; and (iii) provides a principled justification, guidelines, and heuristics for landmark-number estimation, improving the interpretability and practical usability of the framework. The proposed framework and algorithms are evaluated on synthetic and real-world datasets with two types of semantic information, class labels and real-valued feature vectors, and demonstrate superior performance compared to strong baselines.
