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.

翻译：可靠的机器人感知不仅需要预测场景结构，还需识别因观测稀疏或缺失而导致预测不可靠的区域。我们提出ContraMap，一种对比连续映射方法，它通过使用合成噪声样本训练的显式不确定性类别来增强基于核的判别性地图。该公式将未观测区域视为对比类别，能够在不依赖贝叶斯推理的情况下实时联合进行环境预测与空间不确定性估计。基于简单的混合模型视角，我们证明不确定性类别的分配概率是距离感知不确定性代理的单调函数。在二维占据映射、三维语义映射及桌面场景重建中的实验表明，ContraMap在保持建图质量的同时生成空间连贯的不确定性估计，且效率远胜于贝叶斯核地图基线方法。