Robustness control method and system for multi-source fusion positioning

Abstract

The invention provides a robustness control method and system for multi-source fusion localization, and the method comprises the steps: carrying out the visual feature point extraction of the same frame of image data of the same scene, obtaining a first pose through visual inertial navigation fusion, and calculating a visual feature point error and a first re-projection error according to the first pose; carrying out inertial navigation and laser radar feature point extraction on the point cloud data of the same frame in the same scene, and carrying out inertial navigation and laser radar fusion to obtain a second pose; calculating a point cloud feature point error and a second re-projection error according to the second pose; calculating a third pose of the laser radar and the camera, and calculating a camera position feature point error and a third reprojection error according to the third pose; and comparing each re-projection error, carrying out weight ranking on each feature point error, and carrying out quality grade division. Based on the method, the invention further provides a robustness control system. According to the method, the resolving speed of the system can be increased, the environmental adaptability and precision of the system are improved, and the robustness of the system is improved.

Description

technical field [0001] The invention belongs to the technical field of multi-source fusion positioning for unmanned vehicles, and in particular relates to a robust control method and system for multi-source fusion positioning. Background technique [0002] Multi-source fusion positioning technology has become the mainstream technology for position navigation of unmanned vehicles. Multi-source fusion positioning technology effectively solves the shortcomings of single sensor error accumulation and poor stability on unmanned vehicles. Based on information fusion technology, multi-source fusion positioning technology fuses homogeneous or heterogeneous navigation information from different navigation sources according to corresponding fusion algorithms, so as to obtain the best fusion results. Compared with the traditional single navigation source, multi-source fusion navigation can make full use of the advantages of each navigation source, so as to provide the best unmanned veh...

AI Technical Summary

Problems solved by technology

However, these methods cannot guarantee high synchronization accuracy

Different working environments and tasks also have requirements for the selection of sensors. In addition, the combination of sensors is related to the operating environment and tasks, and there are problems such as insufficient information fusion and insufficient use of the advantages of sensor characteristics.

Therefore, the traditional data fusion method lacks a certain degree of self-adaptive ability, and it is difficult to achieve satisfactory performance for unmanned vehicles in complex environments.

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