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  • Session 1
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  • ACRS 1999


    Poster Session 4

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    Development of a personal hybrid positioning system for high density urban areas

    Yusuke Konishi, Ryosuke Shibasaki and Masako Tsuruoka
    Center for Spatial Information Science
    Institute of Industrial Science,
    University of Tokyo
    4-6-1 Komaba,Meguroku,Tokyo 153-8505,Japan
    Tel &Fax :(81)-3-5452-6417
    E-Mail:konishi@skl.iis.u-tokyo.ac.jp

    Keywords: positioning system,personal system,hybrid positioning,accelerometer,gyrometer

    Abstract
    Recently,many researches are being conducted on the personal or hand-held positioning technologies using GPS.However,GPS can ’ t work correctly at the location where the su . cient signal from the GPS satellites can not be received,for example,in the valley between buildings,underground shopping malls.It is strongly demanded to develop a complementary positioning system which can work in the areas where GPS is not available.In this paper,we report the progress of a personal hybrid positioning system which consists ofa pocket-size accelerometer, gyrocompass and a wearable PC.Through several basic experiments,it can be concluded that the positioning system has possibility ofproviding positional information with high accuracy as a complementary system ofGPS even in the areas where GPS signal is not available.

    1 Introduction
    To monitor and control the movement of goods and people,it is neccesary to obtain information about one ’ s position on the real time-basis.Recently,many researches are being conducted on the personal or hand- held positioning technologies using GPS.It is expected that these positioning technologies will be applied to managing and controlling logistics,securing safety of socially weak people,and so forth.

    However,GPS accuracy and reliability is limited in the area where the su . cient signal from the GPS satellites can not be received, for example,in the valley between buildings, underground shopping malls.It is necessary to develop the positioning system which can complement GPS in ” high density ” urban areas.

    In the case of car navigation,a complementary positioning system was already developed and reached practicable level.It is realized by counting number of wheel rotations,sensing steering angles,and matching positioning trajectory to map.On the other hand,there is no effective complementary positioning system for human under the present circumstances,although the development is strongly demanded.

    In this paper,we report the progress of a personal hybrid positioning system which consists of a pocket-size accelerometer,gyrocompass and a wearable PC.

    2 System
    This system consists of a pocket-size accelerometer,gyrocompass (Japan Aviation Electronics,JIMS-30S )and a wearable PC(SII,Ruputer Pro ),as illustrated in Figure 1.



    Figure 1:Personal Hybrid Positioning System

    Both the pocket-size accelerometer and gyrocompass are in a cube whose side is about 3 centimeters,and the cube is fixed to the lower back of user.Signals from these sensors are transmitted through the cable and changed to physical values by the Processor Unit.The Processor Unit is fixed to the waist of user by waist pouch.The Processor Unit and the wearable PC like a wrist watch are also connected by the cable and, the wearable PC is used for computation and indication of the positional information.

    3 Theory
    Viewpoint of this paper is in the motion of walking human ’ s pelvis. At first,by using this positioning system, we measured the motion of pelvis when user walks 6 meters in 10 steps.The time-series data of rotation angles around each axes mesured by the gyrometer are illustrated in Figure 2. The axes are defined in Figure 1. And,we measured the motion of pelvis when user walks along a rectangular course.The time-series data of yaw angle is illustrated in Figure 3.


    Figure 2:The motion of walking human ’ s pelvis measured by the gyrometer



    Figure 3: The time-series data of yaw angle when user walks along a rectangular course

    The experimental data illustrated in Figure 2 and Figure 3 shows that number of steps is computed by the time-series data of pitch angle and direction of walking is computed by the time-series data of yaw angle.

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