让机器人同时建图并识别不确定区域，提升感知可靠性。
ContraMap: Contrastive Uncertainty Mapping for Robot Environment Representation
- 用对比学习显式建模未知区域，实现端到端不确定性估计。
- 在2D/3D场景中保持建图精度，不确定性分布空间一致。
- 比贝叶斯方法快数倍，适合实时机器人应用。
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不可靠的区域。我们提出ContraMap，一种基于核函数的对比连续映射方法，通过合成噪声样本训练显式的不确定性类别，将未观测区域作为对比类处理。该方法可在无需贝叶斯推断的情况下，实时联合完成环境预测与空间不确定性估计。在混合模型视角下，我们证明不确定性类的概率是距离感知不确定性代理的单调函数。在2D占用映射、3D语义映射和桌面场景重建任务中的实验表明，ContraMap在保持建图质量的同时，生成空间连贯的不确定性估计，且相比贝叶斯核映射基线效率显著更高。
原文摘要 · Abstract (English)
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.
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