Indoor Localization with Improved Dead-Reckoning and Particle-Filtering in 2.5D Spaces

• Main Authors: Chen, Yi-Cheng, 陳羿丞
• Other Authors: Tseng, Yu-Chee
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/27061281976913260465

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 101 === Using inertial sensors for pedestrian dead-reckoning (PDR) has attracted considerable attention recently. PDR's main drawback is that accelerometers and gyroscopes are prone to accumulated errors. The Zero Velocity Update (ZUPT) technique tries to identify the moment when the sole is on the ground to calibrate the moving speed. However, it requires that the sensor is mounted near the bottom of the foot, which sometimes results large positioning errors due to the excessive vibration in the measurement of the velocity and orientation (e.q., when users are running). This thesis proposes two self-calibrating PDR models and integrates them with the Indoor 2.5-D Floor Plan model by the Particle Filters. The PDR models called Walking Velocity Update (WUPT) and Running Velocity Update (RUPT), are used for walking and running, respectively. These models require two/three sensors mounted on the upper body/upper leg/lower leg, which collaboratively calibrate the walking/running velocities of users at proper moments. Then, we consider the localization in a multi-floor building by taking these two models with 2.5D floor plan. The particle filters are used to respect the user's potential locations. We observe that the inertial sensors which are carried by users who pass through the special areas (e.g., elevators or stairways) have the identifiable signatures. Thus, we can consider these signatures as the landmarks to calibrate the location estimation. We have developed a prototype and conducted extensive experiments of our models. Results show that the trajectories of WUPT are closer to the original shape when the users are walking. The RUPT performs better than others when the users are running.