The simultaneous localisation and mapping (SLAM) algorithm has drawn increasing interests in autonomous robotic\nsystems. However, SLAM has not been widely explored in embedded system design spaces yet due to the limitation of\nprocessing recourses in embedded systems. Especially when landmarks are not identifiable, the amount of computer\nprocessing will dramatically increase due to unknown data association. In this work, we propose an intelligible SLAM\nsolution for an embedded processing platform to reduce computer processing time using a low-variance resampling\ntechnique. Our prototype includes a low-cost pixy camera, a Robot kit with L298N motor board and Raspberry Pi V2.0.\nOur prototype is able to recognise artificial landmarks in a real environment with an average 75% of identified\nlandmarks in corner detection and corridor detection with only average 1.14 W.
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