Target Recognition Algorithm Based on Optical Sensor Data Fusion
Optical sensor data fusion technology is a research hotspot in the field of information science in recent years, which is widely used in military and civilian fields because of its advantages of high accuracy and low cost, and target recognition is one of the important research directions. Based on...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Hindawi Limited
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/72d7c481fbff44f9a7be70528cb1bf1f |
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| Sumario: | Optical sensor data fusion technology is a research hotspot in the field of information science in recent years, which is widely used in military and civilian fields because of its advantages of high accuracy and low cost, and target recognition is one of the important research directions. Based on the characteristics of small target optical imaging, this paper fully utilizes the frontier theoretical methods in the field of image processing and proposes a small target recognition algorithm process framework based on visible and infrared image data fusion and improves the accuracy as well as stability of target recognition by improving the multisensor information fusion algorithm in the photoelectric meridian tracking system. A practical guide is provided for the solution of the small target recognition problem. To facilitate and quickly verify the multisensor fusion algorithm, a simulation platform for the intelligent vehicle and the experimental environment is built based on Gazebo software, which can realize the sensor data acquisition and the control decision function of the intelligent vehicle. The kinematic model of the intelligent vehicle is firstly described according to the design requirements, and the camera coordinate system, LiDAR coordinate system, and vehicle body coordinate system of the sensors are established. Then, the imaging models of the depth camera and LiDAR, the data acquisition principles of GPS and IMU, and the time synchronization relationship of each sensor are analyzed, and the error calibration and data acquisition experiments of each sensor are completed. |
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