The uses of ambient light for ubiquitous positioning Jingbin Liu, Yuwei Chen, Anttoni Jaakkola, Teemu Hakala, Juha Hyyppä, Liang Chen Department of Remote Sensing and Photogrammetry Finnish Geodetic Institute Masala, 02431, Finland Email: Jingbin.liu@fgi.fi Jian Tang GNSS research Center Wuhan University Wuhan, China Ruizhi Chen Conrad Blucher Institute for Surveying and Science Texas A&M University Corpus Christi Corpus Christi, Texas, USA Hannu Hyyppä Department of Real Estate, Planning and Geoinformatics, Aalto University, Espoo, Finland Abstract—This paper proposed ambient light (ambilight) as a new type of signal sources for positioning. The possibility and methods of ambilight positioning were presented in this paper. It has been shown that two kinds of observables of ambient light can be used for positioning through different principles. Ambilight intensity spectrum measurements have highly location dependency, and they can be used for positioning with the traditional fingerprinting approach. Total ambilight irradiance intensity is used to detect the proximity of a lighting source, and a location solution can be further resolved with the support of knowledge of lighting infrastructure. Ambilight positioning can work in areas where other traditional techniques are not able to function. An ambilight sensor is cost-efficient and miniature in size, and it can be easily integrated with other sensors to form a hybrid positioning system. This paper was concluded with discussions on the possibility, applicability, challenges and outlook of the new ambient light positioning techniques. Keywords—Ambient intelligence; ambient light positionig; ubiquitous positioning; indoor navigation; hybrid positioning; robot localization; location-based service I. INTRODUCTION Ubiquitous positioning is largely required in various applications, location based services (LBS) and ambient intelligence as a consequence of increasing mobility of people and devices across indoor and outdoor environments [1-3]. In particular, a variety of robots is used ever commonly for specific purposes, and they require the support of positioning capability [4]. To achieve ubiquitous positioning under various conditions, a variety of positioning and localization technologies have been developed [5,6]. These positioning technologies can be divided generally into two classes: absolute positioning and relative positioning, which are related to the geospatial property in a geographic reference. Relative positioning methods determine a location relative to another one, while absolute positioning techniques determine an absolute location a specific coordinate system [7]. The global navigation satellite systems (GNSS) are usually considered as absolute positioning systems, and an inertial navigation system (INS) is a good example of relative positioning. The work [6] presented a review of different positioning principles, based on which various positioning technologies have been operated. In general, positioning solution is achieved through resolving the dependence between geographic locations and a group of physical observables. The dependence may be expressed in the form of either deterministic function models or probabilistic models. A deterministic function model expresses the relationship between locations and observables in a closed-form function, which is commonly named as measurement model, and locations can be resolved based on measurement models using, e.g. the least squares or Kalman filters methods. Probabilistic models define the relationship between locations and observables in Bayesian sense, and a family of Bayesian inference methods can be used for resolving positioning solution [6,8]. A ubiquitous positioning solution usually fuses multiple sensors and positioning techniques for an enhanced performance in terms of availability, accuracy and reliability because one of individual technologies commonly has its own limitations of applicability and performance [8,9]. For instance, satellite-based GNSS positioning is the most common technology embedded in navigators and smartphones. GNSS positioning derives the location of user’s receiver based on radio frequency signals transmitted by the satellite systems. However, GNSS positioning remains challenging in urban environments due to signal blockage and multipath effect [10]. GNSS requires a line-of-sight visibility of the satellites, and it is degraded or totally unavailable in blocked environments, e.g. indoors [2,4,5]. As a complement, GNSS is integrated with inertial measurement units (IMU), and the dead reckoning (DR) technique is utilized when GNSS is blocked. However, without the calibration of GNSS, the inertial sensors can only survive alone for a limited duration, e.g. a few of minutes, as their measurements drift over time due to the intrinsic property. Another example is indoor positioning technology using various signals of opportunity (SoOP) [12], which are defined in this paper as signals that are not originally intended for positioning and navigation, and they include radio frequency (RF) signals, e.g. cellular networks, digital television, frequency modulation broadcasting, wireless local area     102