PiLoT: Neural Pixel-to-3D Registration for UAV-based Ego and Target Geo-localization

• URL: http://arxiv.org/abs/2603.20778v3
• Date: Fri, 27 Mar 2026 12:25:16 GMT
• Title: PiLoT: Neural Pixel-to-3D Registration for UAV-based Ego and Target Geo-localization
• Abstract summary: We present PiLoT, a unified framework that tackles UAV-based ego and target geo-localization.<n>We introduce three key contributions: 1) a Dual-Thread Engine that decouples map rendering from core localization thread, ensuring both low latency and drift-free accuracy.<n>This dataset enables the training of a lightweight network that generalizes in a zero-shot manner from simulation to real data.
• Score: 26.426290913965456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present PiLoT, a unified framework that tackles UAV-based ego and target geo-localization. Conventional approaches rely on decoupled pipelines that fuse GNSS and Visual-Inertial Odometry (VIO) for ego-pose estimation, and active sensors like laser rangefinders for target localization. However, these methods are susceptible to failure in GNSS-denied environments and incur substantial hardware costs and complexity. PiLoT breaks this paradigm by directly registering live video stream against a geo-referenced 3D map. To achieve robust, accurate, and real-time performance, we introduce three key contributions: 1) a Dual-Thread Engine that decouples map rendering from core localization thread, ensuring both low latency while maintaining drift-free accuracy; 2) a large-scale synthetic dataset with precise geometric annotations (camera pose, depth maps). This dataset enables the training of a lightweight network that generalizes in a zero-shot manner from simulation to real data; and 3) a Joint Neural-Guided Stochastic-Gradient Optimizer (JNGO) that achieves robust convergence even under aggressive motion. Evaluations on a comprehensive set of public and newly collected benchmarks show that PiLoT outperforms state-of-the-art methods while running over 25 FPS on NVIDIA Jetson Orin platform. Our code and dataset is available at: https://github.com/Choyaa/PiLoT.

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