For localization in daily life, low-cost indoor positioning systems should provide real-time locations with a reasonable accuracy. Considering the flexibility of deployment and low price of iBeacon technique, we develop a real-time fusion workflow to improve localization accuracy of smartphone. First, we propose an iBeacon-based method by integrating a trilateration algorithm with a specific fingerprinting method to resist RSS fluctuations, and obtain accurate locations as the baseline result. Second, as turns are pivotal for positioning, we segment pedestrian trajectories according to turns. Then, we apply a Kalman filter (KF) to heading measurements in each segment, which improves the locations derived by pedestrian dead reckoning (PDR). Finally, we devise another KF to fuse the iBeacon-based approach with the PDR to overcome orientation noises. We implemented this fusion workflow in an Android smartphone and conducted real-time experiments in a building floor. Two different routes with sharp turns were selected. The positioning accuracy of the iBeacon-based method is RMSE 2.75 m. When the smartphone is held steadily, the fusion positioning tests result in RMSE of 2.39 and 2.22 m for the two routes. In addition, the other tests with orientation noises can still result in RMSE of 3.48 and 3.66 m. These results demonstrate our fusion workflow can improve the accuracy of iBeacon positioning and alleviate the influence of PDR drifting.

People often transport objects within indoor environments, who need enough space for the motion. In such cases, the accessibility of indoor spaces relies on the dimensions, which includes a person and her/his operated objects. This paper proposes a new approach to avoid obstacles and compute indoor paths with respect to the user dimension. The approach excludes inaccessible spaces for a user in five steps: (1) compute the minimum distance between obstacles and find the inaccessible gaps; (2) group obstacles according to the inaccessible gaps; (3) identify groups of obstacles that influence the path between two locations; (4) compute boundaries for the selected groups; and (5) build a network in the accessible area around the obstacles in the room. Compared to the Minkowski sum method for outlining inaccessible spaces, the proposed approach generates simpler polygons for groups of obstacles that do not contain inner rings. The creation of a navigation network becomes easier based on these simple polygons. By using this approach, we can create user- and task-specific networks in advance. Alternatively, the accessible path can be generated on the fly before the user enters a room.

An indoor logical network qualitatively represents abstract relationships between indoor spaces, and it can be used for path computation. In this paper, we concentrate on the logical network that does not have notions for metrics. Instead, it relies on the semantics and properties of indoor spaces. A navigation path can be computed by deriving parameters from these semantics and minimizing them in routing algorithms. Although previous studies have adopted semantic approaches to build logical networks, routing methods are seldom elaborated. The main issue with such networks is to derive criteria for path computation using the semantics of spaces. Here, we present a routing mechanism that is based on a dedicated space classification and a set of routing criteria. The space classification reflects characteristics of spaces that are important for navigation, such as horizontal and vertical directions, doors and windows, etc. Six routing criteria are introduced, and they involve: (1) the spaces with the preferred semantics; and/or (2) their centrality in the logical network. Each criterion is encoded as the weights to the nodes or edges of the logical network by considering the semantics of spaces. Logical paths are derived by a traditional shortest-path algorithm that minimizes these weights. Depending on the building’s interior configuration, one criterion may result in several logical paths. Therefore, we introduce a priority ordering of criteria to support path selection and decrease the possible number of logical paths. We provide a proof-of-concept implementation for several buildings to demonstrate the usability of such a routing. The main benefit of this routing method is that it does not need geometric information to compute a path. The logical network can be created using verbal descriptions only, and this routing method can be applied to indoor spaces derived from any building subdivision.