# A Multi-Hypothesis Approach to Pose Ambiguity in Object-Based SLAM

arXiv: https://arxiv.org/abs/2108.01225 · HTML: https://mlsanity.com/paper/2108.01225/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2021.

## Built on by (most cited first, 12 of 12)

- [Uncertainty-aware 3D Object-Level Mapping with Deep Shape Priors](https://mlsanity.com/paper/2309.09118/) (2023)
- [Ambiguity-Aware Multi-Object Pose Optimization for Visually-Assisted Robot Manipulation](https://mlsanity.com/paper/2211.00960/) (2022)
- [Multi-view object pose estimation from correspondence distributions and epipolar geometry](https://mlsanity.com/paper/2210.00924/) (2022)
- [CHSEL: Producing Diverse Plausible Pose Estimates from Contact and Free Space Data](https://mlsanity.com/paper/2305.08042/) (2023)
- [Active 6D Pose Estimation for Textureless Objects using Multi-View RGB Frames](https://mlsanity.com/paper/2503.03726/) (2025)
- [Conditional Variational Autoencoders for Probabilistic Pose Regression](https://mlsanity.com/paper/2410.04989/) (2024)
- [Incremental Non-Gaussian Inference for SLAM Using Normalizing Flows](https://mlsanity.com/paper/2110.00876/) (2021)
- [Object-based SLAM utilizing unambiguous pose parameters considering general symmetry types](https://mlsanity.com/paper/2303.07872/) (2023)
- [Towards Two-view 6D Object Pose Estimation: A Comparative Study on Fusion Strategy](https://mlsanity.com/paper/2207.00260/) (2022)
- [DKPMV: Dense Keypoints Fusion from Multi-View RGB Frames for 6D Pose Estimation of Textureless Objects](https://mlsanity.com/paper/2510.10933/) (2025)
- [AlignPose: Generalizable 6D Pose Estimation via Multi-view Feature-metric Alignment](https://mlsanity.com/paper/2512.20538/) (2025)
- [Temporally Consistent Object 6D Pose Estimation for Robot Control](https://mlsanity.com/paper/2605.02708/) (2026)

## Abstract (arXiv)

In object-based Simultaneous Localization and Mapping (SLAM), 6D object poses offer a compact representation of landmark geometry useful for downstream planning and manipulation tasks. However, measurement ambiguity then arises as objects may possess complete or partial object shape symmetries (e.g., due to occlusion), making it difficult or impossible to generate a single consistent object pose estimate. One idea is to generate multiple pose candidates to counteract measurement ambiguity. In this paper, we develop a novel approach that enables an object-based SLAM system to reason about multiple pose hypotheses for an object, and synthesize this locally ambiguous information into a globally consistent robot and landmark pose estimation formulation. In particular, we (1) present a learned pose estimation network that provides multiple hypotheses about the 6D pose of an object; (2) by treating the output of our network as components of a mixture model, we incorporate pose predictions into a SLAM system, which, over successive observations, recovers a globally consistent set of robot and object (landmark) pose estimates. We evaluate our approach on the popular YCB-Video Dataset and a simulated video featuring YCB objects. Experiments demonstrate that our approach is effective in improving the robustness of object-based SLAM in the face of object pose ambiguity.