Abstract
Robot localization is essential for a wide range of applications, such as navigation, autonomous vehicle, intrusion detection, and so on. This chapter presents a number of localization techniques for both indoor and outdoor robots. The localization problem for an unknown static single target in wireless sensor network is investigated with least squares algorithm and Kalman filter. And an algorithm of passive radio frequency identification (RFID) indoor positioning is proposed based on interval Kalman filter, according to the geometric constraints of responding tags, combined with the target motion information. Next, the simultaneous localization and mapping algorithm (SLAM) for indoor positioning with the low-cost laser LIDAR—RPLIDAR—is investigated. Finally, for outdoor environments, we investigate two integration strategies to fuse inertial navigation system (INS) and vehicle motion sensor (VMS) outputs from their stand-alone configurations. The INS/VMS integration system is an entirely self-contained navigation system, and it is thus expected to benefit the GPS/INS when GPS signals are unavailable for long term.
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Yang, C., Ma, H., Fu, M. (2016). Indoor/Outdoor Robot Localization. In: Advanced Technologies in Modern Robotic Applications. Springer, Singapore. https://doi.org/10.1007/978-981-10-0830-6_9
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DOI: https://doi.org/10.1007/978-981-10-0830-6_9
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