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This paper presents the design and development of a PC-based orientation sensing system using strapdown inertial measurement unit (SIMU). A 9-DOF (degree of freedom) SIMU, which consists of 3-DOF gyroscopes, 3-DOF accelerometers, and 3-DOF magnetometers, are used to estimate the orientation. Data fusion is performed to improve measurement accuracy. In this paper, the orientation measurements are compared and discussed using three different approaches, known as the conventional accelerometer/gyroscope approach, the Kalman filter based 9-DOF SIMU approach, and the gradient decent algorithm based 9-DOF SIMU approach. The derivation of each approach together with the design of the PC-based orientation sensing system is outlined in this paper.

Research toward unmanned mobile robot navigation has gained significant importance in the last decade due to its potential applications in the location-based services industry. The increase in construction of large space indoor buildings has made difficulty for humans to operate within such environments. In this study, a mobile robot's indoor navigation algorithm is developed with vision cameras. Using two monocular cameras (one looking forward and one looking downward), the developed algorithms make use of the salient features of the environments to estimate rotational and translational motions for real-time positioning of the mobile robot. At the same time, an algorithm based on artificial landmark recognition is developed. The artificial landmark is shaped arrow based signboards with different colors representing different paths. These algorithms are integrated into a designed framework for mobile robot real-time positioning and autonomous navigation. Experiments are performed to validate the designed system using the mobile robot PIONEER P3-AT. The developed algorithm was able to detect and extract artificial landmark information up to 3 m distance for the mobile robot guidance. Experiment results show an average error of 0.167 m deviation from the ideal path, signified the good ability and performance of the development autonomous navigation algorithm.

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