One of the main shortcomings of received signal strength-based indoor localisation techniques is the labour and time cost involved in acquiring labelled `ground-truth' training data. This training data is often obtained through fingerprinting, which involves visiting all prescribed locations to capture sensor observations throughout the environment. In this work, the authors present a helmet for localisation optimisation (H4LO): a low-cost robotic system designed to cut down on said labour by utilising an off-the-shelf light detection and ranging device. This system allows for simultaneous localisation and mapping, providing the human user with accurate pose estimation and a corresponding map of the environment. The high-resolution location estimation can then be used to train a positioning model, where received signal strength data is acquired from a human-worn wearable device. The method is evaluated using live measurements, recorded within a residential property. They compare the groundtruth location labels generated automatically by the H4LO system with a camera-based fingerprinting technique from previous work. They find that the system remains comparable in performance to the less efficient camera-based method, whilst removing the need for time-consuming labour associated with registering the user's location.

中文翻译：

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H4LO：使用SLAM派生的地图和姿势进行高效RF指纹识别的自动化平台

基于接收信号强度的室内定位技术的主要缺点之一是获取带标签的“地面真相”训练数据所涉及的人工和时间成本。该训练数据通常是通过指纹获取的，其中包括访问所有规定的位置以捕获整个环境中的传感器观测结果。在这项工作中，作者提出了一种用于定位优化的头盔（H4LO）：一种低成本的机器人系统，旨在通过利用现成的光检测和测距设备来减少上述工作量。该系统允许同时进行定位和地图绘制，从而为人类用户提供准确的姿势估计和相应的环境图。然后可以使用高分辨率位置估计来训练定位模型，从人体可穿戴设备获取接收到的信号强度数据。该方法使用记录在住宅物业中的实时测量进行评估。他们将H4LO系统自动生成的地面真实位置标签与先前工作中基于摄像头的指纹技术进行了比较。他们发现，该系统在性能上可与效率较低的基于摄像头的方法媲美，同时消除了与注册用户位置相关的耗时工作的需要。