Learning Joint Semantic-Geometric Uncertainty for Structured Prediction with Closed-Loop Calibration

Structured prediction in dynamic, open-world environments—such as online High-Definition (HD) map construction for autonomous driving, mobile robotics, and embodied AI—fundamentally relies on the precise alignment of continuous geometry and discrete semantics. However, existing methods typically decouple these heterogeneous modalities, producing "overconfident yet erroneous" predictions that lack reliable uncertainty calibration in degraded environments. To address this fundamental limitation, we propose IntroMap, a probabilistic framework dedicated to learning joint semantic-geometric uncertainty with closed-loop calibration. At its core, the Semantic-Geometric Joint Calibration (SGJC) mechanism explicitly captures the aleatoric co-occurrence between spatial coordinates and semantic proxies via a Hybrid 3D Multivariate Gaussian distribution. Furthermore, to overcome the shortcomings of passive, open-loop uncertainty estimation, we introduce the Closed-Loop Feature Calibration (CLFC) module. It transforms the predicted joint covariance into latent modulation signals, actively rectifying degraded feature representations. Extensive experiments on the nuScenes and Argoverse 2 datasets demonstrate IntroMap's model-agnostic generalizability across multiple baselines (e.g., MapTRv2, MapQR), achieving state-of-the-art accuracy—including 63.1\% mAP on nuScenes and a striking 8.1\% mAP leap under challenging rainy conditions. Crucially, downstream end-to-end autonomous driving evaluations verify that our calibrated joint uncertainty establishes explicit safety margins, effectively preventing motion planners from blindly trusting hallucinated features and significantly reducing collision rates in long-tail scenarios. Code is available at https://anonymous.4open.science/r/IntroMap-F0BD/.