Precise localization of mobile robots in uncertain environments is a fundamental and crucial issue in robotics. In this paper, to deal with the unbounded accumulated errors of dead reckoning (DR)-based localization, wireless sensor network (WSN)-based localization is applied to calibrate the uncertainty of odometry using a Kalman filter (KF). In addition, to further aid in obtaining precise positions and reduce uncertainty, a novel backward dead reckoning (BDR) localization approach is proposed. The experimental results demonstrate the success and reliability of the proposed method.
is a Ph.D. student in the Department of Computer Science and Engineering at Texas A&M University. He received his bachelor's degree in Systems and Control from Nanjing University, China, in 2013. His research includes GNSS system modeling for high confidence requirements such as integrity checking, multipath avoidance, and computational geometry.
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