# ContraMap: Contrastive Uncertainty Mapping for Robot Environment Representation

arXiv: https://arxiv.org/abs/2603.27632 · HTML: https://mlsanity.com/paper/2603.27632/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 29 Mar 2026.

## Builds on (most cited first, 9 of 9)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)
- [Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness](https://mlsanity.com/paper/2006.10108/) (2020)
- [Denoising diffusion models for out-of-distribution detection](https://mlsanity.com/paper/2211.07740/) (2022)
- [Unified Classification and Rejection: A One-versus-All Framework](https://mlsanity.com/paper/2311.13355/) (2023)
- [V-PRISM: Probabilistic Mapping of Unknown Tabletop Scenes](https://mlsanity.com/paper/2403.08106/) (2024)
- [Analysis of Confident-Classifiers for Out-of-distribution Detection](https://mlsanity.com/paper/1904.12220/) (2019)
- [DOSE3 : Diffusion-based Out-of-distribution detection on SE(3) trajectories](https://mlsanity.com/paper/2502.16725/) (2025)

## Abstract (arXiv)

Reliable robot perception requires not only predicting scene structure, but also identifying where predictions should be treated as unreliable due to sparse or missing observations. We present ContraMap, a contrastive continuous mapping method that augments kernel-based discriminative maps with an explicit uncertainty class trained using synthetic noise samples. This formulation treats unobserved regions as a contrastive class, enabling joint environment prediction and spatial uncertainty estimation in real time without Bayesian inference. Under a simple mixture-model view, we show that the probability assigned to the uncertainty class is a monotonic function of a distance-aware uncertainty surrogate. Experiments in 2D occupancy mapping, 3D semantic mapping, and tabletop scene reconstruction show that ContraMap preserves mapping quality, produces spatially coherent uncertainty estimates, and is substantially more efficient than Bayesian kernelmap baselines.