Dynamic object identification and tracking method based on laser radar

By combining the data of lidar, millimeter-wave radar and optical image sensors, combined with deep learning and Bayesian filtering methods, the problem of attenuation of lidar detection capability and difficulty in data synchronization in bad weather is solved, and high-precision identification and tracking of dynamic objects is achieved, and the reliability and accuracy of the system is improved.

CN119716892APending Publication Date: 2025-03-28SAIEN LINGDONG (SHANGHAI) INTELLIGENT TECH CO LTD
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Patent Information

Application Number
CN202411857512.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-17
Publication Date
2025-03-28

AI Technical Summary

Technical Problem

The detection capability of the laser radar in the prior art is attenuated under severe weather conditions, the data volume is large and difficult to synchronize, and the millimeter-wave radar cannot observe the Z coordinate data, which limits its object recognition ability in three-dimensional space, especially inaccurate recognition of humans.

Method used

Using data from lidar, millimeter-wave radar and optical image sensors in combination, data preprocessing and feature extraction are performed through deep learning models, and real-time state estimation of multi-sensor data fusion and Bayesian filtering methods are performed.

Benefits of technology

It improves the system's perception ability, reduces false alarms and missed detection, enhances the reliability and accuracy of the system, and maintains high data accuracy and system performance especially in severe weather conditions.

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Abstract

The invention discloses a dynamic object identification and tracking method based on a laser radar. The method comprises the following steps: S1, collecting data; s2, extracting features from the optical image data, extracting speed and distance information from the millimeter wave radar, and enhancing the expression ability of the features by using a deep learning method and the like; s3, fusing the point cloud data of the laser radar with the camera image, and further processing the fused data; s4, fusing a plurality of target lists to obtain reliable and real target information, and avoiding misinformation and missing detection; s5, comprehensively combining the decisions obtained by the multiple sensors by adopting a fusion strategy, and obtaining a fusion result; and S6, estimating and updating the state of the sensor carrier in real time by using a Bayesian filtering method. According to the invention, identification and tracking of the dynamic object are realized. The system has the beneficial effects that the sensing capability of the system is improved, false alarm and missing detection are reduced, and the reliability and the accuracy of the system are enhanced.
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