Statistical-Based Control Points Selection for Indoor Illuminance Measurement

The distribution of indoor illuminance affects the health and the performance of humans and is, therefore, subject to audits. Audits are performed after each modification of the light sources, and the currently adopted approach uses a human operator to perform measurements manually. This process is time-consuming, and we, therefore, aim to automate it with a mobile platform. In order to automate the process, we propose an online algorithm for selecting the control points. The algorithm is iterative, and it takes previous measurements into account in order to select the control points adaptively. We show how the proposed method can be combined with a mobile platform to perform the audit autonomously. Initially, the mobile platform, equipped with a laser range finder, is used to build the floor plan of the room. This floor plan is then used to restrict the area within which the control points can be selected. The whole measurement process is then executed without the presence or the intervention of a human operator, and this reduces the time needed for the audit. We have performed simulated and real experiments to demonstrate the performance of the proposed approach.