Semantic Semi-Incremental Data-Association-Free Object SLAM
This paper presents a generalized data-association-free SLAM framework that jointly estimates data associations, robot poses, landmark positions, and landmark semantics using deep learning semantic information (e.g., class labels and feature vectors). It adopts a semi-incremental estimation scheme for better accuracy and efficiency, and provides principled guidance for landmark-number estimation. Evaluations on synthetic and real datasets show superior performance over strong baselines with two types of semantic information.
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[Submitted on 25 Jul 2026]
Title:Semantic Semi-Incremental Data-Association-Free Object SLAM
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Abstract: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.
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.23384 [cs.RO]
(or arXiv:2607.23384v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.23384
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yihao Zhang [view email] [v1] Sat, 25 Jul 2026 22:31:31 UTC (27,424 KB)
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