# A Variational Observation Model of 3D Object for Probabilistic Semantic SLAM

arXiv: https://arxiv.org/abs/1809.05225 · HTML: https://mlsanity.com/paper/1809.05225/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2018.

## Built on by (most cited first, 5 of 5)

- [Anytime 3D Object Reconstruction using Multi-modal Variational Autoencoder](https://mlsanity.com/paper/2101.10391/) (2021)
- [Anchor Distance for 3D Multi-Object Distance Estimation from 2D Single Shot](https://mlsanity.com/paper/2101.10399/) (2021)
- [LangGS-SLAM: Real-Time Language-Feature Gaussian Splatting SLAM](https://mlsanity.com/paper/2602.06991/) (2026)
- [Self-supervised Learning of 3D Object Understanding by Data Association and Landmark Estimation for Image Sequence](https://mlsanity.com/paper/2104.07077/) (2021)
- [Advancing Frontiers in SLAM: A Survey of Symbolic Representation and Human-Machine Teaming in Environmental Mapping](https://mlsanity.com/paper/2405.01398/) (2024)

## Abstract (arXiv)

We present a Bayesian object observation model for complete probabilistic semantic SLAM. Recent studies on object detection and feature extraction have become important for scene understanding and 3D mapping. However, 3D shape of the object is too complex to formulate the probabilistic observation model; therefore, performing the Bayesian inference of the object-oriented features as well as their pose is less considered. Besides, when the robot equipped with an RGB mono camera only observes the projected single view of an object, a significant amount of the 3D shape information is abandoned. Due to these limitations, semantic SLAM and viewpoint-independent loop closure using volumetric 3D object shape is challenging. In order to enable the complete formulation of probabilistic semantic SLAM, we approximate the observation model of a 3D object with a tractable distribution. We also estimate the variational likelihood from the 2D image of the object to exploit its observed single view. In order to evaluate the proposed method, we perform pose and feature estimation, and demonstrate that the automatic loop closure works seamlessly without additional loop detector in various environments.