[2603.27632] ContraMap: Contrastive Uncertainty Mapping for Robot Environment Representation

Computer Science > Robotics arXiv:2603.27632 (cs) [Submitted on 29 Mar 2026] Title:ContraMap: Contrastive Uncertainty Mapping for Robot Environment Representation Authors:Chi Cuong Le, Weiming Zhi View a PDF of the paper titled ContraMap: Contrastive Uncertainty Mapping for Robot Environment Representation, by Chi Cuong Le and 1 other authors View PDF HTML (experimental) Abstract: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. Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2603.27632 [cs.RO]...