# Consensus-Informed Optimization Over Mixtures for Ambiguity-Aware Object SLAM

arXiv: https://arxiv.org/abs/2107.09265 · HTML: https://mlsanity.com/paper/2107.09265/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2021.

## Built on by (most cited first, 3 of 3)

- [3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation](https://mlsanity.com/paper/2302.03744/) (2023)
- [Discrete-Continuous Smoothing and Mapping](https://mlsanity.com/paper/2204.11936/) (2022)
- [Incremental Non-Gaussian Inference for SLAM Using Normalizing Flows](https://mlsanity.com/paper/2110.00876/) (2021)

## Abstract (arXiv)

Building object-level maps can facilitate robot-environment interactions (e.g. planning and manipulation), but objects could often have multiple probable poses when viewed from a single vantage point, due to symmetry, occlusion or perceptual failures. A robust object-level simultaneous localization and mapping (object SLAM) algorithm needs to be aware of this pose ambiguity. We propose to maintain and subsequently disambiguate the multiple pose interpretations to gradually recover a globally consistent world representation. The max-mixtures model is applied to implicitly and efficiently track all pose hypotheses, but the resulting formulation is non-convex, and therefore subject to local optima. To mitigate this problem, temporally consistent hypotheses are extracted, guiding the optimization into the global optimum. This consensus-informed inference method is applied online via landmark variable re-initialization within an incremental SLAM framework, iSAM2, for robust real-time performance. We demonstrate that this approach improves SLAM performance on both simulated and real object SLAM problems with pose ambiguity.