Gaussian Mixture-Sound Field Landmark Model for Robot Localization Talker: Prof. Jwu-Sheng Hu Department of Electrical and Control Engineering National Chiao-Tung University Hsinchu, Taiwan Outline Introduction Overall System Architecture Robot Localization Methodology Architecture
Talker: Prof. Jwu-Sheng Hu
Department of Electrical and Control EngineeringNational Chiao-Tung UniversityHsinchu, Taiwan
Photo of eRobot
Overall System Diagram
The overall system contains a dog-like pet robot (named “eRobot”)
and a robot localization agent mounted in an arbitrarily indoor position
Photo of tiny network bridge module on the robot Localization
The overall system including the PC-side remote control Localization.
Robot localization methodology architecture
where and are the weighting factors, and are the phase difference and magnitude ratio GMM relatively.
The environment is complexly and it contains a partition room.
The SNR ranges of the three different conditions
A processed frame and overlapping condition
Experimental result in condition one using GM-SFLM
Experimental result in condition two using GM-SFLM
Experimental result in condition three using GM-SFLM