An intelligible implementation of FastSLAM2.0 on a low-power embedded architecture
Abstract 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 landmar...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Jiménez Serrata, Albert A. [verfasserIn] Yang, Shufan [verfasserIn] Li, Renfa [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: EURASIP journal on embedded systems - Heidelberg : Springer, 2006, 2017(2017), 1 vom: 02. März |
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| Übergeordnetes Werk: |
volume:2017 ; year:2017 ; number:1 ; day:02 ; month:03 |
| Links: |
|---|
| DOI / URN: |
10.1186/s13639-017-0075-9 |
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SPR032062710 |
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10.1186/s13639-017-0075-9 doi (DE-627)SPR032062710 (SPR)s13639-017-0075-9-e DE-627 ger DE-627 rakwb eng 004 ASE 54.27 bkl 54.52 bkl Jiménez Serrata, Albert A. verfasserin aut An intelligible implementation of FastSLAM2.0 on a low-power embedded architecture 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract 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. Simultaneous localisation and mapping (dpeaa)DE-He213 Robotics (dpeaa)DE-He213 Embedded systems (dpeaa)DE-He213 Pixy camera (dpeaa)DE-He213 Yang, Shufan verfasserin aut Li, Renfa verfasserin aut Enthalten in EURASIP journal on embedded systems Heidelberg : Springer, 2006 2017(2017), 1 vom: 02. März (DE-627)511637217 (DE-600)2233384-8 1687-3963 nnns volume:2017 year:2017 number:1 day:02 month:03 https://dx.doi.org/10.1186/s13639-017-0075-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 54.27 ASE 54.52 ASE AR 2017 2017 1 02 03 |
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10.1186/s13639-017-0075-9 doi (DE-627)SPR032062710 (SPR)s13639-017-0075-9-e DE-627 ger DE-627 rakwb eng 004 ASE 54.27 bkl 54.52 bkl Jiménez Serrata, Albert A. verfasserin aut An intelligible implementation of FastSLAM2.0 on a low-power embedded architecture 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract 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. Simultaneous localisation and mapping (dpeaa)DE-He213 Robotics (dpeaa)DE-He213 Embedded systems (dpeaa)DE-He213 Pixy camera (dpeaa)DE-He213 Yang, Shufan verfasserin aut Li, Renfa verfasserin aut Enthalten in EURASIP journal on embedded systems Heidelberg : Springer, 2006 2017(2017), 1 vom: 02. März (DE-627)511637217 (DE-600)2233384-8 1687-3963 nnns volume:2017 year:2017 number:1 day:02 month:03 https://dx.doi.org/10.1186/s13639-017-0075-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 54.27 ASE 54.52 ASE AR 2017 2017 1 02 03 |
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10.1186/s13639-017-0075-9 doi (DE-627)SPR032062710 (SPR)s13639-017-0075-9-e DE-627 ger DE-627 rakwb eng 004 ASE 54.27 bkl 54.52 bkl Jiménez Serrata, Albert A. verfasserin aut An intelligible implementation of FastSLAM2.0 on a low-power embedded architecture 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract 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. Simultaneous localisation and mapping (dpeaa)DE-He213 Robotics (dpeaa)DE-He213 Embedded systems (dpeaa)DE-He213 Pixy camera (dpeaa)DE-He213 Yang, Shufan verfasserin aut Li, Renfa verfasserin aut Enthalten in EURASIP journal on embedded systems Heidelberg : Springer, 2006 2017(2017), 1 vom: 02. März (DE-627)511637217 (DE-600)2233384-8 1687-3963 nnns volume:2017 year:2017 number:1 day:02 month:03 https://dx.doi.org/10.1186/s13639-017-0075-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 54.27 ASE 54.52 ASE AR 2017 2017 1 02 03 |
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10.1186/s13639-017-0075-9 doi (DE-627)SPR032062710 (SPR)s13639-017-0075-9-e DE-627 ger DE-627 rakwb eng 004 ASE 54.27 bkl 54.52 bkl Jiménez Serrata, Albert A. verfasserin aut An intelligible implementation of FastSLAM2.0 on a low-power embedded architecture 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract 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. Simultaneous localisation and mapping (dpeaa)DE-He213 Robotics (dpeaa)DE-He213 Embedded systems (dpeaa)DE-He213 Pixy camera (dpeaa)DE-He213 Yang, Shufan verfasserin aut Li, Renfa verfasserin aut Enthalten in EURASIP journal on embedded systems Heidelberg : Springer, 2006 2017(2017), 1 vom: 02. März (DE-627)511637217 (DE-600)2233384-8 1687-3963 nnns volume:2017 year:2017 number:1 day:02 month:03 https://dx.doi.org/10.1186/s13639-017-0075-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 54.27 ASE 54.52 ASE AR 2017 2017 1 02 03 |
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Jiménez Serrata, Albert A. |
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An intelligible implementation of FastSLAM2.0 on a low-power embedded architecture |
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Abstract 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. |
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Abstract 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. |
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Abstract 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. |
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