MAPLE-RF：部分探索环境中的高效概率射频源定位

中文摘要 AI 辅助

从接收信号中定位射频（RF）发射器通常需要环境模型来预测障碍物如何阻挡和反射信号。然而，在许多机器人应用中，尤其是在机器人使用同步定位与建图（SLAM）进行探索的同时定位信号源时，只能获得部分地图。我们研究了在这种部分探索地图上的单快照发射器定位问题，并比较了两种输出发射器位置后验的方法。第一种方法将数字孪生方法（对每个候选位置进行光线追踪）扩展到部分地图，方法是将未探索空间视为自由空间，并在混合地图覆盖率上训练。第二种方法，即MAPLE-RF，将估计的路径到达角和信噪比编码为与地图已知性、占用率和视线可见性对齐的网格通道，U-Net在一次前向传播中对所有候选位置进行评分，而无需在推理时模拟传播。室内环境的光线追踪模拟表明，在混合地图覆盖率上训练对两种方法都至关重要。数字孪生方法在大多数单快照指标上更准确，而MAPLE-RF在查询成本上接近，其查询成本不依赖于传播模型，比使用通用光线追踪的全网格查询低两个数量级以上。两种方法均优于高斯和高斯混合基线，并且在由其自身估计引导的探索路径上，融合的MAPLE-RF后验在信号源附近放置的概率比所比较的方法更高。代码和数据将公开发布。

英文摘要

Localizing a radio-frequency (RF) transmitter from received signals often requires a model of the environment to predict how obstacles block and reflect the signal. In many robotic applications, however, only a partial map is available, particularly when a robot localizes the source while exploring with simultaneous localization and mapping (SLAM). We study single-snapshot transmitter localization on such partially explored maps and compare two approaches that output a posterior over transmitter locations. The first extends a digital-twin method, which ray-traces every candidate location, to partial maps by treating unexplored space as free and training on mixed map coverage. The second, MAPLE-RF, encodes estimated path angles of arrival and signal-to-noise ratios as grid channels aligned with map knownness, occupancy, and line-of-sight visibility, and a U-Net scores all candidate positions in one pass without simulating propagation at inference. Ray-tracing simulations of indoor environments indicate that training on mixed map coverage is essential for both approaches. The digital-twin approach is more accurate on most single-snapshot metrics, while MAPLE-RF comes close at a query cost that does not depend on the propagation model and is more than two orders of magnitude below a fresh full-grid query with general-purpose ray tracing. Both outperform Gaussian and Gaussian-mixture baselines, and on exploration routes guided by its own estimates, fused MAPLE-RF posteriors place more probability near the source than the compared methods. Code and data will be released.

发表机构

• New York University（纽约大学）

机构由 AI 辅助整理，请以论文原文为准。