Dynamic risk assessment method combining visual positioning and mapping and control barrier function

By combining visual positioning and mapping and control barrier functions, and using multi-sensor data for preprocessing and semantic segmentation, the problem that the prior art is difficult to deal with dynamic obstacles in complex dynamic environments is solved, and more accurate environmental understanding and risk assessment is achieved, which significantly improves navigation performance and safety.

CN119691513APending Publication Date: 2025-03-25SHENZHEN POLYTECHNIC
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Patent Information

Application Number
CN202411808631.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-10
Publication Date
2025-03-25

AI Technical Summary

Technical Problem

When facing complex dynamic environments, existing visual SLAM technologies are difficult to deal with dynamic obstacles in a timely manner, and environmental understanding and risk assessment methods are insufficient.

Method used

Combining visual positioning and mapping and control barrier functions, multi-sensor data is used for preprocessing and semantic segmentation, fusion features are extracted and map-constructed, and drone position estimation and obstacle risk assessment are carried out based on this, and navigation paths are dynamically adjusted.

Benefits of technology

It improves the handling ability of dynamic obstacles, improves the semantic understanding of the environment and the accuracy of risk assessment, and significantly improves the navigation performance and safety of the system in complex dynamic environments.

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Abstract

The invention is suitable for the technical field of unmanned aerial vehicle positioning, and particularly relates to a dynamic risk assessment method combining visual positioning and mapping and a control barrier function, and the method comprises the steps: collecting multi-sensor data, carrying out the preprocessing of the multi-sensor data, and extracting fusion data; semantic segmentation is carried out on the fusion data, fusion features are extracted, and mapping is carried out; performing pose estimation on the unmanned aerial vehicle based on the multi-sensor data, and performing risk assessment on the obstacle to obtain a risk assessment result; dynamically adjusting the navigation path of the unmanned aerial vehicle according to the risk assessment result. According to the method, the processing capacity of dynamic obstacles is improved, the semantic comprehension of the environment and the accuracy of risk assessment are improved by introducing an advanced algorithm, so that the navigation performance and safety of the system in the complex dynamic environment are remarkably improved, the adaptive capacity of an automatic system to environmental changes can be greatly improved, and the method is suitable for large-scale popularization and application. Revolutionary improvements are brought to the fields of automatic driving, unmanned aerial vehicle monitoring, robot navigation and the like.
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