Rao-Blackwellized Particle Filter Algorithm Integrated with Neural Network Sensor Model Using Laser Distance Sensor
Commonly, simultaneous localization and mapping (SLAM) algorithm is developed using high-end sensors. Alternatively, some researchers use low-end sensors due to the lower cost of the robot. However, the low-end sensor produces noisy sensor measurements that can affect the SLAM algorithm, which is pr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Micromachines |
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| Online Access: | https://www.mdpi.com/2072-666X/14/3/560 |
| _version_ | 1797610042544881664 |
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| author | Amirul Jamaludin Norhidayah Mohamad Yatim Zarina Mohd Noh Norlida Buniyamin |
| author_facet | Amirul Jamaludin Norhidayah Mohamad Yatim Zarina Mohd Noh Norlida Buniyamin |
| author_sort | Amirul Jamaludin |
| collection | DOAJ |
| description | Commonly, simultaneous localization and mapping (SLAM) algorithm is developed using high-end sensors. Alternatively, some researchers use low-end sensors due to the lower cost of the robot. However, the low-end sensor produces noisy sensor measurements that can affect the SLAM algorithm, which is prone to error. Therefore, in this paper, a SLAM algorithm, which is a Rao-Blackwellized particle filter (RBPF) integrated with artificial neural networks (ANN) sensor model, is introduced to improve the measurement accuracy of a low-end laser distance sensor (LDS) and subsequently improve the performance of SLAM. The RBPF integrated with the ANN sensor model is experimented with by using the Turtlebot3 mobile robot in simulation and real-world experiments. The experiment is validated by comparing the occupancy grid maps estimated by RBPF integrated with the ANN sensor model and RBPF without ANN. Both the results in simulation and real-world experiments show that the SLAM performance of RBPF integrated with the ANN sensor model is better than the RBPF without ANN. In the real-world experiment results, the performance of the occupied cells integrated with the ANN sensor model is increased by 107.59%. In conclusion, the SLAM algorithm integrated with the ANN sensor model is able to improve the accuracy of the map estimate for mobile robots using low-end LDS sensors. |
| first_indexed | 2024-03-11T06:09:54Z |
| format | Article |
| id | doaj.art-0dc6398e39d54474b29ffdad1faf0670 |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-11T06:09:54Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-0dc6398e39d54474b29ffdad1faf06702023-11-17T12:42:31ZengMDPI AGMicromachines2072-666X2023-02-0114356010.3390/mi14030560Rao-Blackwellized Particle Filter Algorithm Integrated with Neural Network Sensor Model Using Laser Distance SensorAmirul Jamaludin0Norhidayah Mohamad Yatim1Zarina Mohd Noh2Norlida Buniyamin3Centre for Telecommunication Research & Innovation (CeTRI), Fakulti Kejuruteraan Elektronik & Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal 76100, Melaka, MalaysiaCentre for Telecommunication Research & Innovation (CeTRI), Fakulti Kejuruteraan Elektronik & Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal 76100, Melaka, MalaysiaCentre for Telecommunication Research & Innovation (CeTRI), Fakulti Kejuruteraan Elektronik & Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal 76100, Melaka, MalaysiaFaculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam 40450, Selangor, MalaysiaCommonly, simultaneous localization and mapping (SLAM) algorithm is developed using high-end sensors. Alternatively, some researchers use low-end sensors due to the lower cost of the robot. However, the low-end sensor produces noisy sensor measurements that can affect the SLAM algorithm, which is prone to error. Therefore, in this paper, a SLAM algorithm, which is a Rao-Blackwellized particle filter (RBPF) integrated with artificial neural networks (ANN) sensor model, is introduced to improve the measurement accuracy of a low-end laser distance sensor (LDS) and subsequently improve the performance of SLAM. The RBPF integrated with the ANN sensor model is experimented with by using the Turtlebot3 mobile robot in simulation and real-world experiments. The experiment is validated by comparing the occupancy grid maps estimated by RBPF integrated with the ANN sensor model and RBPF without ANN. Both the results in simulation and real-world experiments show that the SLAM performance of RBPF integrated with the ANN sensor model is better than the RBPF without ANN. In the real-world experiment results, the performance of the occupied cells integrated with the ANN sensor model is increased by 107.59%. In conclusion, the SLAM algorithm integrated with the ANN sensor model is able to improve the accuracy of the map estimate for mobile robots using low-end LDS sensors.https://www.mdpi.com/2072-666X/14/3/560SLAMoccupancy grid mapartificial neural networklaser distance sensorparticle filter |
| spellingShingle | Amirul Jamaludin Norhidayah Mohamad Yatim Zarina Mohd Noh Norlida Buniyamin Rao-Blackwellized Particle Filter Algorithm Integrated with Neural Network Sensor Model Using Laser Distance Sensor Micromachines SLAM occupancy grid map artificial neural network laser distance sensor particle filter |
| title | Rao-Blackwellized Particle Filter Algorithm Integrated with Neural Network Sensor Model Using Laser Distance Sensor |
| title_full | Rao-Blackwellized Particle Filter Algorithm Integrated with Neural Network Sensor Model Using Laser Distance Sensor |
| title_fullStr | Rao-Blackwellized Particle Filter Algorithm Integrated with Neural Network Sensor Model Using Laser Distance Sensor |
| title_full_unstemmed | Rao-Blackwellized Particle Filter Algorithm Integrated with Neural Network Sensor Model Using Laser Distance Sensor |
| title_short | Rao-Blackwellized Particle Filter Algorithm Integrated with Neural Network Sensor Model Using Laser Distance Sensor |
| title_sort | rao blackwellized particle filter algorithm integrated with neural network sensor model using laser distance sensor |
| topic | SLAM occupancy grid map artificial neural network laser distance sensor particle filter |
| url | https://www.mdpi.com/2072-666X/14/3/560 |
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