For mobile robots and position-based services, precise localization is the most fundamental capability while path-planning is an important application based on that. To realize that, the cost and the performance of sensors are of great concern. We first introduce a low-calculation-cost signal encoding and decomposition method. A low-cost localization and path-planning solution based on a novel Visible Light Communication (VLC) system for indoor environments is then introduced afterwards. A number of modulated LED lights are used as beacons to aid indoor localization additional to illumination. Each LED has a unique modulation code and flashes at a same frequency, nevertheless no synchronization working conditions are required for signal decomposition, which further cut down the overall co...[ Read more ]

For mobile robots and position-based services, precise localization is the most fundamental capability while path-planning is an important application based on that. To realize that, the cost and the performance of sensors are of great concern. We first introduce a low-calculation-cost signal encoding and decomposition method. A low-cost localization and path-planning solution based on a novel Visible Light Communication (VLC) system for indoor environments is then introduced afterwards. A number of modulated LED lights are used as beacons to aid indoor localization additional to illumination. Each LED has a unique modulation code and flashes at a same frequency, nevertheless no synchronization working conditions are required for signal decomposition, which further cut down the overall cost of the system. Three main technical parts comprise our system: light signal decomposition, localization and path-planning. Specifically, a Gold-sequence-based tiny-length beacon codes selection method is introduced. Accordingly, an asynchronous blind signal decomposition method is created for the modulated light signals decomposition to achieve robust signal feature extraction. A Gaussian Process (GP) is used to model the intensity distributions of the light sources. A Bayesian localization framework is constructed using the results of the GP, leading to precise localization. Path-planning is hereby feasible by only using the GP variance field, rather than using a metric map. Graph-based path-planners are introduced to cope with the practical situations. Any smart device with a low-cost photonic sensor could then localize itself precisely in real time. We demonstrate our localization and path-planning system by real-time experiments performed on a tablet PC in an indoor environment. In contrast to existing widely-applied indoor localization approaches, like vision-based and laser-based methods, our approach reveals its advantages as low-cost, globally consistent and it retains the potential applications using VLC.