Positioning confidence optimization method based on Bayesian multi-sensor error constraint

Abstract

The invention discloses a positioning confidence optimization method based on Bayesian multi-sensor error constraint, and belongs to the technical field of navigation positioning and information fusion application. According to the method, a multi-source fusion positioning system composed of a GNSS, an IMU, laser radar, and a visual camera is utilized, and on the basis of a multi-source fusion state updating model, error constraints of all sensors are extracted; a Bayesian decision equation is introduced into a sliding window of a time sequence, and weight balancing is carried out on each information source; and a joint global error constraint equation is established, a self-adaptive fusion positioning framework is optimized, and a multi-source fusion optimal confidence positioning solution is obtained. The method is simple and easy to implement, and a self-adaptive positioning technical means can be provided for an unmanned intelligent platform in unknown shielding environments such as urban canyons and geological natural disasters.

Description

technical field [0001] The invention relates to a positioning position reliability optimization method based on Bayesian multi-sensor error constraints, and belongs to the technical field of navigation positioning and information fusion applications. Background technique [0002] With the rapid development of unmanned logistics, smart agriculture, assisted driving and other unmanned platforms, high-precision positioning services are gradually moving towards more ubiquitous unknown and complex scenarios. Unmanned platforms are an important basis for safe travel and intelligent operations. However, restricted scenarios such as urban canyons, tunnels, and mountain valleys limit the ubiquitous and seamless applications of unmanned platforms. In view of the fact that visual feature tracking is limited by large-scale, light and other open natural environments, lidar scene recognition ability is poor, it is difficult to make closed-loop global correction, low-cost inertial navigati...

AI Technical Summary

Problems solved by technology

[0002] With the rapid development of unmanned logistics, smart agriculture, assisted driving and other unmanned platforms, high-precision positioning services are gradually moving towards more ubiquitous unknown and complex scenarios. An important foundation for unmanned platforms to travel safely and intelligently. However, restricted scenarios such as urban canyons, tunnels, and mountain valleys limit the ubiquitous and seamless applications of unmanned platforms.

In view of the fact that visual feature tracking is limited by large-scale, light and other open natural environments, lidar scene recognition ability is poor, it is difficult to make closed-loop global correction, low-cost inertial navigation accumulated error time drift, GNSS signal anti-interference ability is weak, and it is easy to be blocked. The development of multi-sensor loose / tight coupling positioning methods combined with GNSS\IMU\vision\LiDAR is the mainstream means to solve the problem of high-precision navigation and positioning in restricted environments

[0003] At present, although the known multi-source loose\tight coupling technology has achieved certain results, the actual technical application is limited to structured environments such as urban roads, industrial parks, and airports, and the optimization of positioning results depends on the strong constraints of prior maps. , the technology is difficult to apply and promote in an unknown environment

Navigation and positioning technology based on multi-source fusion is a research hotspot in the field of unmanned intelligence at this stage, but there are few achievements in the usability level of navigation and positioning in multi-type sheltered environments, and there is no perfect fusion theoretical basis and unified positioning usability evaluation. method, it is urgent to develop research on multi-source fusion weight adaptive adjustment and source confidence probability evaluation technology for complex occlusion environments, but there is no related application in the prior art

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