In this article, we study the Cyber-Human Interaction (CHI) based approach that the "Human" part sets a list of location-based objectives and makes the pathway decision whereas the "Cyber" part provides the pathway suggestion, infer heuristics from the environment along the pathway and incrementally resolve the location-based objectives with new heuristics for indoor localization. For this study, we implement a CHI-based system on mobile phone. The CHI-based system offers the pathway suggestion and the solution of the location-based objectives based on its trajectory management. Without any priori knowledge on the area of interest and any aid from other equipments, a laborer can achieve his location-based objectives by walking through the area of interest and simultaneously online interacting with the CHI-based system installed in his phone. In evaluation, we conduct the experiments and show the advantage CHI in reducing the time cost and the expense cost for the laborer.
@cite_0: In this paper we describe a new Bayesian estimation approach for simultaneous mapping and localization for pedestrians based on odometry with foot mounted inertial sensors. When somebody walks within a constrained area such as a building, then even noisy and drift-prone odometry measurements can give us information about features like turns, doors, and walls, which we can use to build a form of a map of the explored area, especially when these features are revisited over time. Our initial results for our novel scheme which we call "FootSLAM" are very surprising in that true SLAM with stable relative positioning accuracy of 1-2 meters for pedestrians is indeed possible based on inertial sensors alone without any prior known building indoor layout. Furthermore, the 2D maps obtained even for just 10 minutes of walking converge to a good approximation of the true layout forming the basis for future automated collaborative mapping of buildings.
@cite_21: WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization.
There has been much work on dealing with the SLAM problem in robotics fields. Usually, in the scenario of SLAM, a robot is designated to explore the area of interest for simultaneous localization and mapping with equipped sensors (e.g. camera, laser and sonar). The localization problem is to determine the relative location of the robot in the area, and the mapping problem is to locate the objects surrounding the robot. For example, FootSLAM @cite_0 built the floor plan with shoe-mounted IMU sensors, and WiFi-SLAM @cite_21 used the Gaussian process latent variable model to build RSS-based connectivity graphs.