Driven by the rapid growth of the low-altitude economy, integrated sensing and communication (ISAC) technologies are essential to meet the stringent demands for reliable connectivity and situational awareness. Within this context, multipath-based simultaneous localization and mapping has emerged as a promising approach by leveraging radio frequency (RF) multipath to reconstruct environment maps alongside agent localization. Nevertheless, existing studies largely confine themselves to bistatic non-line-of-sight links and assume purely specular reflections from smooth surfaces, overlooking the monostatic sensing capability inherent in ISAC systems and the diffuse scattering effects induced by non-ideal outdoor building facades. To address these limitations, this paper presents the first Bayesian multipath-based environment mapping framework for ISAC that integrates monostatic and bistatic measurements under non-ideal surface propagation. We establish geometric relationships linking both sensing modes to a common reflective surface, enabling their association with the same physical feature for data-level fusion. Building on this formulation, we design two complementary Bayesian frameworks with corresponding factor-graph representations, allowing flexible adaptation to different scene requirements. The effectiveness of the proposed approach is validated through synthetic RF data, demonstrating that the fusion of monostatic and bistatic links consistently yields environment maps with higher accuracy, greater robustness and faster convergence than single-link baselines.

翻译：在低空经济快速发展的推动下，集成感知与通信（ISAC）技术对于满足可靠连接与态势感知的严苛需求至关重要。在此背景下，基于多径的同步定位与建图方法通过利用射频多径在实现智能体定位的同时重建环境地图，已成为一种颇具前景的途径。然而，现有研究大多局限于双站非视距链路，并假设来自光滑表面的纯镜面反射，忽略了ISAC系统固有的单站感知能力以及非理想户外建筑立面引起的漫散射效应。为应对这些局限，本文提出了首个面向ISAC的、在非理想表面传播条件下融合单站与双站测量的贝叶斯多径环境建图框架。我们建立了将两种感知模式与同一反射表面关联的几何关系，使其能够与同一物理特征相关联，从而实现数据级融合。基于此公式，我们设计了两种互补的贝叶斯框架及其对应的因子图表示，能够灵活适应不同的场景需求。通过合成射频数据验证了所提方法的有效性，结果表明：与单链路基线相比，融合单站与双站链路始终能生成具有更高精度、更强鲁棒性和更快收敛速度的环境地图。