# POV-SLAM: Probabilistic Object-Aware Variational SLAM in Semi-Static Environments

arXiv: https://arxiv.org/abs/2307.00488 · HTML: https://mlsanity.com/paper/2307.00488/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2023.

## Built on by (most cited first, 6 of 6)

- [ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning](https://mlsanity.com/paper/2309.16650/) (2023)
- [Toward General Object-level Mapping from Sparse Views with 3D Diffusion Priors](https://mlsanity.com/paper/2410.05514/) (2024)
- [Gaussian Mapping for Evolving Scenes](https://mlsanity.com/paper/2506.06909/) (2025)
- [Changes in Real Time: Online Scene Change Detection with Multi-View Fusion](https://mlsanity.com/paper/2511.12370/) (2025)
- [RoboAtlas: Contextual Active SLAM](https://mlsanity.com/paper/2606.26046/) (2026)
- [Build Once, Monitor Continuously: Persistent Semantic Mapping via Autonomous Exploration and Open-Vocabulary Object Updates](https://mlsanity.com/paper/2409.15493/) (2024)

## Abstract (arXiv)

Simultaneous localization and mapping (SLAM) in slowly varying scenes is important for long-term robot task completion. Failing to detect scene changes may lead to inaccurate maps and, ultimately, lost robots. Classical SLAM algorithms assume static scenes, and recent works take dynamics into account, but require scene changes to be observed in consecutive frames. Semi-static scenes, wherein objects appear, disappear, or move slowly over time, are often overlooked, yet are critical for long-term operation. We propose an object-aware, factor-graph SLAM framework that tracks and reconstructs semi-static object-level changes. Our novel variational expectation-maximization strategy is used to optimize factor graphs involving a Gaussian-Uniform bimodal measurement likelihood for potentially-changing objects. We evaluate our approach alongside the state-of-the-art SLAM solutions in simulation and on our novel real-world SLAM dataset captured in a warehouse over four months. Our method improves the robustness of localization in the presence of semi-static changes, providing object-level reasoning about the scene.