Bionic polarization synchronous localization and mapping method based on factor graph optimization

Abstract

The invention discloses a bionic polarization synchronous localization and mapping method based on factor graph optimization. The method comprises the steps of: selecting the state of an integrated navigation system, building a radar odometer and polarized light camera measurement model and an inertial navigation system motion model, and initializing the integrated navigation system; according to the state node position constraint, the adjacent state node pose transformation constraint and the state node heading constraint of a carrier at the current moment, establishing an integrated navigation system state and measurement node error function to obtain a to-be-optimized factor graph; optimizing the to-be-optimized factor graph by using a Gauss-Newton method to obtain optimal estimated values of the position, the speed and the attitude of the carrier under the world coordinate system with the minimum error function; and updating and synchronizing the optimal estimated values and the map to realize localization and mapping of the carrier. Inertia, polarization and radar odometer multi-source information are fused and complemented, and the method has the characteristic of being free of external interference; and due to the characteristics of the historical information of factor graph optimization measurement and state, the environment adaptability is good, the anti-interference performance is high, and the localization and mapping precision is high.

Description

technical field [0001] The invention belongs to the category of autonomous navigation technology, and relates to a bionic polarization synchronous positioning and mapping (SLAM) method based on factor graph optimization. Background technique [0002] SLAM is the abbreviation of Simultaneous Localization and Mapping, which means "simultaneous positioning and mapping". It refers to the process in which the moving body calculates its own position and constructs an environmental map based on the information of the sensor. At present, SLAM technology is mainly used in aerospace, intelligent transportation, Internet of Things, VR and AR and other fields. [0003] SLAM research focuses on using filter theory to minimize the estimation error covariance of the moving body pose and the landmark points of the map. Generally, the output of the inertial navigation system is used as the input of the prediction process, and the output of the laser sensor is used as the input of the update...

AI Technical Summary

Problems solved by technology

[0005] The main technical problem to be solved: the purpose of the present invention is how to introduce polarized light information, and apply the graph optimization method to SLAM to solve the difficulties in determining its own position, poor environmental adaptability, low anti-interference, and positioning problems in simultaneous positioning and composition. Problems related to lack of precise composition

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