# Semantic Semi-Incremental Data-Association-Free Object SLAM

arXiv: https://arxiv.org/abs/2607.23384 · HTML: https://mlsanity.com/paper/2607.23384/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 25 Jul 2026.

## Builds on (most cited first, 13 of 13)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [ODAM: Object Detection, Association, and Mapping using Posed RGB Video](https://mlsanity.com/paper/2108.10165/) (2021)
- [Probabilistic Data Association via Mixture Models for Robust Semantic SLAM](https://mlsanity.com/paper/1909.11213/) (2019)
- [SLAM with Objects using a Nonparametric Pose Graph](https://mlsanity.com/paper/1704.05959/) (2017)
- [Probabilistic Data Association for Semantic SLAM at Scale](https://mlsanity.com/paper/2202.12802/) (2022)
- [Discrete-Continuous Smoothing and Mapping](https://mlsanity.com/paper/2204.11936/) (2022)
- [Semantic Enhancement for Object SLAM with Heterogeneous Multimodal Large Language Model Agents](https://mlsanity.com/paper/2411.06752/) (2024)
- [Opti-Acoustic Semantic SLAM with Unknown Objects in Underwater Environments](https://mlsanity.com/paper/2403.12837/) (2024)
- [Data-Association-Free Landmark-based SLAM](https://mlsanity.com/paper/2302.13264/) (2023)
- [Globally Optimal Data-Association-Free Landmark-Based Localization Using Semidefinite Relaxations](https://mlsanity.com/paper/2504.08547/) (2025)

## Abstract (arXiv)

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.