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AbsIracf-All mobile robots suffer from odometry error. Relative localization from odometry has both the systematic and the non-systematic errors. However, once a precise system error model and its parameters are given, the accuracy of odometry can be remarkably improved. Most previous works on this effort focused on the differential drive robots with little(More)
—In this paper, a robust algorithm that discriminates various eye motions from the ElectroOculoGram (EOG) signals is proposed. Previous researches that use the EOG only focused on saccadic motions or blinks. However, we cover all eye motions including double/triple blinks and left/right winks. Furthermore, we suggest a novel method, which removes noises of(More)
All mobile bases suffer from localization errors. Previous approaches to accommodate for localization errors either use external sensors such as lasers or sonars, or use internal sensors like encoders. An encoder's information is integrated to derive the robot's position; this is called odometry. A combination of external and internal sensors will(More)
In this paper, we propose an algorithmic compass that yields the heading information of a mobile robot using the vanishing point in indoor environments: VPass. With the VPass, a loop-closing effect (which is a significant reduction of errors by revisiting a known place through a loop) can be achieved even for a loop-less environment. From the implementation(More)