A Particle PHD Filter for Dynamic Grid Map Building towards Indoor Environment
The PHD (Probability Hypothesis Density) filter is a sub-optimal multi-target Bayesian filter based on a random finite set, which is widely used in the tracking and estimation of dynamic objects in outdoor environments. Compared with the outdoor environment, the indoor environment space and the shap...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/15/6891 |
| _version_ | 1797525856983187456 |
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| author | Yanjie Liu Changsen Zhao Yanlong Wei |
| author_facet | Yanjie Liu Changsen Zhao Yanlong Wei |
| author_sort | Yanjie Liu |
| collection | DOAJ |
| description | The PHD (Probability Hypothesis Density) filter is a sub-optimal multi-target Bayesian filter based on a random finite set, which is widely used in the tracking and estimation of dynamic objects in outdoor environments. Compared with the outdoor environment, the indoor environment space and the shape of dynamic objects are relatively small, which puts forward higher requirements on the estimation accuracy and response speed of the filter. This paper proposes a method for fast and high-precision estimation of the dynamic objects’ velocity for mobile robots in an indoor environment. First, the indoor environment is represented as a dynamic grid map, and the state of dynamic objects is represented by its grid cells state as random finite sets. The estimation of dynamic objects’ speed information is realized by using the measurement-driven particle-based PHD filter. Second, we bound the dynamic grid map to the robot coordinate system and derived the update equation of the state of the particles with the movement of the robot. At the same time, in order to improve the perception accuracy and speed of the filter for dynamic targets, the CS (Current Statistical) motion model is added to the CV (Constant Velocity) motion model, and interactive resampling is performed to achieve the combination of the advantages of the two. Finally, in the Gazebo simulation environment based on ROS (Robot Operating System), the speed estimation and accuracy analysis of the square and cylindrical dynamic objects were carried out respectively when the robot was stationary and in motion. The results show that the proposed method has a great improvement in effect compared with the existing methods. |
| first_indexed | 2024-03-10T09:18:58Z |
| format | Article |
| id | doaj.art-8b514bfa8f524176b21fee657cd2d3a8 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:18:58Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8b514bfa8f524176b21fee657cd2d3a82023-11-22T05:21:12ZengMDPI AGApplied Sciences2076-34172021-07-011115689110.3390/app11156891A Particle PHD Filter for Dynamic Grid Map Building towards Indoor EnvironmentYanjie Liu0Changsen Zhao1Yanlong Wei2State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, ChinaState Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, ChinaState Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, ChinaThe PHD (Probability Hypothesis Density) filter is a sub-optimal multi-target Bayesian filter based on a random finite set, which is widely used in the tracking and estimation of dynamic objects in outdoor environments. Compared with the outdoor environment, the indoor environment space and the shape of dynamic objects are relatively small, which puts forward higher requirements on the estimation accuracy and response speed of the filter. This paper proposes a method for fast and high-precision estimation of the dynamic objects’ velocity for mobile robots in an indoor environment. First, the indoor environment is represented as a dynamic grid map, and the state of dynamic objects is represented by its grid cells state as random finite sets. The estimation of dynamic objects’ speed information is realized by using the measurement-driven particle-based PHD filter. Second, we bound the dynamic grid map to the robot coordinate system and derived the update equation of the state of the particles with the movement of the robot. At the same time, in order to improve the perception accuracy and speed of the filter for dynamic targets, the CS (Current Statistical) motion model is added to the CV (Constant Velocity) motion model, and interactive resampling is performed to achieve the combination of the advantages of the two. Finally, in the Gazebo simulation environment based on ROS (Robot Operating System), the speed estimation and accuracy analysis of the square and cylindrical dynamic objects were carried out respectively when the robot was stationary and in motion. The results show that the proposed method has a great improvement in effect compared with the existing methods.https://www.mdpi.com/2076-3417/11/15/6891mobile robotparticle PHD filterlocal dynamic grid mapCV motion modelCS motion model |
| spellingShingle | Yanjie Liu Changsen Zhao Yanlong Wei A Particle PHD Filter for Dynamic Grid Map Building towards Indoor Environment Applied Sciences mobile robot particle PHD filter local dynamic grid map CV motion model CS motion model |
| title | A Particle PHD Filter for Dynamic Grid Map Building towards Indoor Environment |
| title_full | A Particle PHD Filter for Dynamic Grid Map Building towards Indoor Environment |
| title_fullStr | A Particle PHD Filter for Dynamic Grid Map Building towards Indoor Environment |
| title_full_unstemmed | A Particle PHD Filter for Dynamic Grid Map Building towards Indoor Environment |
| title_short | A Particle PHD Filter for Dynamic Grid Map Building towards Indoor Environment |
| title_sort | particle phd filter for dynamic grid map building towards indoor environment |
| topic | mobile robot particle PHD filter local dynamic grid map CV motion model CS motion model |
| url | https://www.mdpi.com/2076-3417/11/15/6891 |
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