Semantic Semi-Incremental Data-Association-Free Object SLAM
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...
| Authors | Yihao Zhang, Jungseok Hong, John J. Leonard |
|---|---|
| Primary category | cs.RO |
| Published | 25 July 2026 |
| Last updated at source | 25 July 2026 |
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