This paper describes a method for localizing wireless mobile clients in a multistory building using a public wireless Local Area Network (LAN) system. Physical location data on personal devices and mobile robots is important to information services and robot applications. Wireless mobile clients are localized in a multistory building using public wireless LAN access points placed, three-dimensionally in the building. Information on the floor number and client location is acquired probabilistically, with estimation providing a probabilistic model for localization based on sparse Bayesian learning. Results of experiments confirm the feasibility of our proposal.
The authors have been developing a mobile robot with sensors for various services in the university campus. A prominent feature of university campus is a substantial amount of pedestrians in the outdoor environment. This feature is also typical in the shopping streets where cars are shutout. This paper proposes an application of a stochastic model for the observation and state transition for detecting mobile objects while the localization process. This model can be treated in the framework of nonlinear Kalman filter. In this paper, we implemented the detection algorithm in the offline mode. We demonstrate the experimental detection results, which validate the usefulness of the proposed algorithm.
This paper describes a method for location estimation of mobile wireless local area network (LAN) clients in multistory buildings using the strength of the received signals in a state space framework. Data pertaining to the physical positions of personal electronic devices or mobile robots are important for information services and robotic applications. We focus on integrating the estimation results with other sensor data based on a state space framework. The estimation model for location provides a variance of a mobile client's location. We integrate the estimation results and the motion results of the mobile client using a Kalman filter. The estimation model is re-initialized when the mobile client moves to another floor in the building by detecting the change in the floor number where the mobile client has moved. This is done by using the Bayesian inference. Experimental results show the feasibility of this method.
This paper proposes a method of pose (position and orientation) fitting of construction components in a construction site for automated handling based on the relation between components (parts) and their information (packets). Robots can acquire the required information of the component via the environmentattached storages, such as RFID devices. When an ID reader identifies an ID device, it should take some pose in its communication range. This fact may bring the idea of estimating the pose of a component that carries the device. In this idea, only single device identification cannot fix the pose of the component. We define the conditions of the ID reader and ID devices for the pose fitting, and propose a fitting method with at least two different identifications where two devices are not attached to the same plane or parallel planes of the component. Several examples of pose fitting show the feasibility of our idea.
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