行人扩散：用于惯性导航的多模态生成去噪和密集状态估计
PedestrianDiffusion: Multimodal Generative Denoising and Dense State Estimation for Inertial Navigation
AI总结：
针对消费级惯性导航受MEMS随机噪声限制问题，提出PedestrianDiffusion框架，将密集6D状态估计转化为连续条件去噪过程，引入零样本语义条件机制，用ODE求解器提升性能，在多基准测试中达先进水平。
摘要：
消费级惯性导航的精度受微机电系统（MEMS）的随机噪声限制。传统确定性神经架构常因“估计抖动”而牺牲高频运动保真度以换取数值稳定性。我们提出了PedestrianDiffusion，这是一个多模态谱域生成框架，将密集6D状态估计重新表述为连续条件去噪过程。通过在频域中操作，我们的公式限制了谱协方差，作为一种数学预处理器来稳定反向扩散轨迹。此外，我们引入了一种零样本语义条件机制，利用视觉语言嵌入作为分类先验，以推广到异构传感器噪声分布。为了解决生成跟踪的计算难题，我们部署了单步确定性概率流ODE求解器（$T = 1$）。这产生了高容量异步批轨迹细化，确立了生成架构在边缘硬件上进行异步批轨迹细化的可行性。在OxIOD、RIDI、RoNIN和TLIO基准上的广泛评估表明，PedestrianDiffusion实现了领先的性能，对脉冲扰动和耦合6D运动漂移表现出前所未有的鲁棒性。这项工作为下一代神经惯性测量单元（N-IMUs）提供了一个严格的算法蓝图。
英文摘要：
The accuracy of consumer-grade inertial navigation is bottlenecked by the stochastic noise of Micro-Electro-Mechanical Systems (MEMS). Traditional deterministic neural architectures often succumb to ``estimation jittering,'' sacrificing high-frequency kinematic fidelity for numerical stability. We propose PedestrianDiffusion, a multimodal spectral-domain generative framework reformulating dense 6D state estimation as a continuous conditional denoising process. By operating in the frequency domain, our formulation bounds the spectral covariance, acting as a mathematical preconditioner to stabilize the reverse diffusion trajectory. Furthermore, we introduce a zero-shot semantic conditioning mechanism leveraging vision-language embeddings as categorical priors to generalize across heterogeneous sensor noise profiles. To address the computational intractability of generative tracking, we deploy a single-step deterministic probability flow ODE solver ($T=1$). This yields high-capacity asynchronous batch trajectory refinement, establishing the viability of generative architectures for asynchronous batch trajectory refinement on edge hardware. Extensive evaluations on the OxIOD, RIDI, RoNIN, and TLIO benchmarks demonstrate that PedestrianDiffusion achieves state-of-the-art performance, exhibiting unprecedented robustness to impulse perturbations and coupled 6D kinematic drift. This work provides a rigorous algorithmic blueprint for next-generation Neural Inertial Measurement Units (N-IMUs).
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