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Abstract

This paper presents a low-cost global pose localization method using least-squares method and extended Kalman filter (EKF) for an anthropomorphous dual-arm mobile robot (ADAMR) driven by omnidirectional Mecanum wheels in indoor environments. This method is developed by fusing measurements from the KINECT sensor and four encoders mounted on the omnidirectional Mecanum wheels. The KINECT sensor is used to recognize landmarks in the working environments of the robot, and then obtain the azimuth angles and distances between these landmarks and the robot. Based on these measured information, a static global pose initialization algorithm using least-squares method is applied to estimate both unknown start-up position and orientation of the robot. Once the initial pose has been roughly determined, an EKF approach is proposed to fuse the odometric measurements from the four encoders and the azimuth angles and distances from the KINECT sensor to corresponding landmarks. Simulations are conducted to show performance of the proposed method. Experimental results are used to illustrate that the proposed method provides accurate static estimation of both unknown initial poses of the robot.

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