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Indoor/Outdoor Robot Localization

  • Chenguang YangEmail author
  • Hongbin MaEmail author
  • Mengyin Fu
Chapter

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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Copyright information

© Science Press and Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.Key Lab of Autonomous Systems and Networked Control, Ministry of EducationSouth China University of TechnologyGuangzhouChina
  2. 2.Centre for Robotics and Neural SystemsPlymouth UniversityDevonUK
  3. 3.School of AutomationBeijing Institute of TechnologyBeijingChina
  4. 4.State Key Lab of Intelligent Control and Decision of Complex SystemBeijing Institute of TechnologyBeijingChina

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