Over the past decade, visible light positioning has become increasingly important for precise localization systems, yet its widespread adoption is limited due to the necessity of modifying existing lighting systems. This paper presents HueLoc, a novel method that bypasses this issue by using inherent features of light, such as the dominant colours in white LED lights, and employs affordable, energy-efficient hue sensors for location services. We propose that by extracting the power at dominant wavelengths of LEDs, these can be uniquely identified using a specifically designed signature. The unique signatures can be used by mobile objects for spatial awareness and further localization using the proposed regression-based learning approach. Our experiments demonstrate that HueLoc attains a location-mapping accuracy of 100% and achieves decimeter-level localization precision with a moving object in uncontrolled lighting conditions. Moreover, these unique signatures can be combined with other RF-based technologies to enhance their localization accuracy. As an example, this paper details the integration of Bluetooth features with light signatures using a three-stage incremental learning approach.

Visible Light Positioning (VLP) has been prevalent in providing high-precision localization systems in the past decade. However, the commercial availability or usage is still limited primarily due to the requirement of changing the existing lighting infrastructure. In this paper, we propose HueSense, an alternative technique to develop a passive VLP system by extracting light-emission intrinsic features, such as dominant colours present in the white LED light. The method can eliminate the need to change lighting-infrastructure, and only uses cheaper and power-efficient off-the-shelf hue sensors. Our experiments demonstrate that HueSense can achieve a location-mapping accuracy of 80.14% with a moving robot in uncontrolled lighting environments.