# Probabilistic Data Association via Mixture Models for Robust Semantic SLAM

arXiv: https://arxiv.org/abs/1909.11213 · HTML: https://mlsanity.com/paper/1909.11213/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2019.

## Built on by (most cited first, 16 of 16)

- [Situational Graphs for Robot Navigation in Structured Indoor Environments](https://mlsanity.com/paper/2202.12197/) (2022)
- [Semantic-SuPer: A Semantic-aware Surgical Perception Framework for Endoscopic Tissue Identification, Reconstruction, and Tracking](https://mlsanity.com/paper/2210.16674/) (2022)
- [Advances in Inference and Representation for Simultaneous Localization and Mapping](https://mlsanity.com/paper/2103.05041/) (2021)
- [From SLAM to Situational Awareness: Challenges and Survey](https://mlsanity.com/paper/2110.00273/) (2021)
- [Semantic Visual Simultaneous Localization and Mapping: A Survey](https://mlsanity.com/paper/2209.06428/) (2022)
- [An Online Semantic Mapping System for Extending and Enhancing Visual SLAM](https://mlsanity.com/paper/2203.03944/) (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)
- [LOSS-SLAM: Lightweight Open-Set Semantic Simultaneous Localization and Mapping](https://mlsanity.com/paper/2404.04377/) (2024)
- [Incremental Non-Gaussian Inference for SLAM Using Normalizing Flows](https://mlsanity.com/paper/2110.00876/) (2021)
- [SemHint-MD: Learning from Noisy Semantic Labels for Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2303.18219/) (2023)
- [CLOVER: Context-aware Long-term Object Viewpoint- and Environment- Invariant Representation Learning](https://mlsanity.com/paper/2407.09718/) (2024)
- [Open-Set Semantic Uncertainty Aware Metric-Semantic Graph Matching](https://mlsanity.com/paper/2409.11555/) (2024)
- [SGBA: Semantic Gaussian Mixture Model-Based LiDAR Bundle Adjustment](https://mlsanity.com/paper/2410.01618/) (2024)
- [Advancing Frontiers in SLAM: A Survey of Symbolic Representation and Human-Machine Teaming in Environmental Mapping](https://mlsanity.com/paper/2405.01398/) (2024)
- [Semantic Semi-Incremental Data-Association-Free Object SLAM](https://mlsanity.com/paper/2607.23384/) (2026)

## Abstract (arXiv)

Modern robotic systems sense the environment geometrically, through sensors like cameras, lidar, and sonar, as well as semantically, often through visual models learned from data, such as object detectors. We aim to develop robots that can use all of these sources of information for reliable navigation, but each is corrupted by noise. Rather than assume that object detection will eventually achieve near perfect performance across the lifetime of a robot, in this work we represent and cope with the semantic and geometric uncertainty inherent in methods like object detection. Specifically, we model data association ambiguity, which is typically non-Gaussian, in a way that is amenable to solution within the common nonlinear Gaussian formulation of simultaneous localization and mapping (SLAM). We do so by eliminating data association variables from the inference process through max-marginalization, preserving standard Gaussian posterior assumptions. The result is a max-mixture-type model that accounts for multiple data association hypotheses as well as incorrect loop closures. We provide experimental results on indoor and outdoor semantic navigation tasks with noisy odometry and object detection and find that the ability of the proposed approach to represent multiple hypotheses, including the "null" hypothesis, gives substantial robustness advantages in comparison to alternative semantic SLAM approaches.