Research Output
An intelligible implementation of FastSLAM2.0 on a low-power embedded architecture
  The simultaneous localisation and mapping (SLAM) algorithm has drawn increasing interests in autonomous robotic systems. However, SLAM has not been widely explored in embedded system design spaces yet due to the limitation of processing recourses in embedded systems. Especially when landmarks are not identifiable, the amount of computer processing will dramatically increase due to unknown data association. In this work, we propose an intelligible SLAM solution for an embedded processing platform to reduce computer processing time using a low-variance resampling technique. Our prototype includes a low-cost pixy camera, a Robot kit with L298N motor board and Raspberry Pi V2.0. Our prototype is able to recognise artificial landmarks in a real environment with an average 75% of identified landmarks in corner detection and corridor detection with only average 1.14 W.

  • Type:


  • Date:

    02 March 2017

  • Publication Status:


  • Publisher

    Springer Science and Business Media LLC

  • DOI:


  • Cross Ref:


  • Funders:

    National Natural Science Foundation of China


Jiménez Serrata, A. A., Yang, S., & Li, R. (2017). An intelligible implementation of FastSLAM2.0 on a low-power embedded architecture. EURASIP Journal on Embedded Systems, 2017(1),



Simultaneous localisation and mapping, Robotics, Embedded systems, Pixy camera

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    An Intelligible Implementation Of FastSLAM2.0 On A Low-power Embedded Architecture


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