{
  "annotation_id": "545032c4-396b-4146-871a-629b0cc2b271",
  "date_created": "2026-02-17T05:53:07.775000Z",
  "date_modified": "2026-02-17T05:53:07.775000Z",
  "file_hash": "66a01d5f0631717890a831a76bdbdc9c98e46d3c0ab3f1c7ff904b838ecf434a",
  "private": false,
  "record": {
    "abstract": "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.",
    "arxiv_id": "2601.06602",
    "authors": [
      "Mohammed S. Alharbi",
      "Shinkyu Park"
    ],
    "categories": [
      "cs.RO",
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "UMLoc: Uncertainty-Aware Map-Constrained Inertial Localization with Quantified Bounds",
    "url": "https://arxiv.org/abs/2601.06602",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "338fa922-8fae-4757-8157-c351ab1c5138",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}