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Autonomous cleaning robots increasingly gain popularities for saving time and reduce household labor. Less expensive self-localization of the robot is one of the most important problems in popularizing service robot products. However, trade-offs exist between making less expensive self-localization systems and the quality at which they perform. Relative localizations that utilize low-cost gyroscope (hereafter refer to as a gyro) based on technology referred to as Micro Electro-Mechanical System (MEMS) with odometry sensors have emerged as standalone, and robust to environment changes (Barshan & DurrantWhyte, 1995; De Cecco, 2003; Jetto et al., 1995). However, the performance characteristics associated with these low-cost MEMS gyros are limited by various error sources (Yazdi et al., 1998; Chung et al., 2001; Hong, 1999). These errors are subdivided into two main categories according to their spectral signature: errors that change rather rapidly (shortterm) and rather slowly (long-term) (Skaloud et al., 1999). Due to these error sources, the accumulated angular error grows considerably over time and provides a fundamental limitation to any angle measurement that relies solely on integration of rate signals from gyros (Hong, 2003; Stilwell et al., 2001). Currently, because of their potential for unbounded growth of errors, odometry and gyro can only be used in conjunction with external sensors that provide periodic absolute position/attitude updates (Barshan & Durrant-Whyte, 1995; De Cecco, 2003; Jetto et al., 1995). However, such aiding localization schemes not only increase installation and operating costs for the whole system, but also only lead to compensation of the long-term inertial errors. That is, the angle deterioration due to quickly changing errors (system noise, vibration) is not detectable by external sensors over a short time period (Sun et al., 2005; Hong, 2008a). Considering this concept, better performance of a localization system can be expected if short-term noise in the gyro signal is suppressed prior to integration. In the previous study (Hong, 2008a; Hong & Park, 2008b), it has been shown that the threshold 27

Heading Measurements for Indoor Mobile Robots with Minimized Drift Using a MEMS Gyroscopes