Inertial localization is particularly valuable in GPS-denied environments such as indoors. However, localization using only Inertial Measurement Units (IMUs) suffers from drift caused by motion-process noise and sensor biases. This paper introduces Uncertainty-aware Map-constrained Inertial Localization (UMLoc), an end-to-end framework that jointly models IMU uncertainty and map constraints to achieve drift-resilient positioning. UMLoc integrates two coupled modules: (1) a Long Short-Term Memory (LSTM) quantile regressor, which estimates the specific quantiles needed to define 68%, 90%, and 95% prediction intervals serving as a measure of localization uncertainty and (2) a Conditioned Generative Adversarial Network (CGAN) with cross-attention that fuses IMU dynamic data with distance-based floor-plan maps to generate geometrically feasible trajectories. The modules are trained jointly, allowing uncertainty estimates to propagate through the CGAN during trajectory generation. UMLoc was evaluated on three datasets, including a newly collected 2-hour indoor benchmark with time-aligned IMU data, ground-truth poses and floor-plan maps. Results show that the method achieves a mean drift ratio of 5.9% over a 70 m travel distance and an average Absolute Trajectory Error (ATE) of 1.36 m, while maintaining calibrated prediction bounds.

翻译：惯性定位在室内等GPS拒止环境中具有重要价值。然而，仅使用惯性测量单元（IMU）的定位会受到运动过程噪声和传感器偏差引起的漂移影响。本文提出不确定性感知地图约束惯性定位（UMLoc），这是一个端到端框架，通过联合建模IMU不确定性与地图约束来实现抗漂移定位。UMLoc集成两个耦合模块：（1）长短期记忆（LSTM）分位数回归器，用于估计定义68%、90%和95%预测区间所需的具体分位数，作为定位不确定性的度量；（2）具有交叉注意力的条件生成对抗网络（CGAN），该网络融合IMU动态数据与基于距离的平面布局图，以生成几何可行的轨迹。两个模块联合训练，使得不确定性估计能够在轨迹生成过程中通过CGAN传播。我们在三个数据集上评估了UMLoc，包括一个新收集的2小时室内基准数据集，该数据集包含时间对齐的IMU数据、真实位姿和平面布局图。结果表明，该方法在70米行程距离上实现了5.9%的平均漂移比和1.36米的平均绝对轨迹误差（ATE），同时保持校准的预测边界。