Smart Terminal Target Positioning Using Spatial Anchor Points
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Solution Overview
Problem
Current positioning technologies in Extended Reality (XR) environments, such as AR and VR, provide only approximate directions or region prompts, failing to achieve accurate point-to-point positioning, which no longer meets user needs for precise location identification.
Innovation Solution
A method for target positioning using a smart terminal that involves obtaining image data of the surrounding environment, generating a spatial image, determining anchor point information based on the spatial image, and using this information to accurately locate targets, incorporating lidar and depth sensors for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional positioning technologies are used in XR environments, then the system complexity remains low, but the positioning precision is insufficient and cannot achieve accurate point-to-point positioning
Solution Approach 1:
The positioning system is segmented into multiple functional modules: image data acquisition module (using lidar and/or depth sensors), spatial image generation module, anchor point information extraction module, and positioning calculation module. Each module performs a specific function, allowing the complex positioning task to be divided into manageable steps that improve accuracy without overwhelming system complexity
Solution Approach 2:
The patent transitions from traditional 2D image-based positioning to 3D spatial positioning by introducing depth information through lidar point cloud data or depth sensor measurements. This dimensional enhancement enables accurate point-to-point positioning in three-dimensional XR spaces, resolving the limitation of approximate region-based positioning
2Measurement precision
If multiple sensors (lidar, depth sensor) are introduced to improve positioning accuracy, then the measurement precision increases, but the device complexity and cost increase
Solution Approach 1:
The patent designs a multi-functional sensing system where lidar and depth sensors can serve multiple purposes: spatial mapping, object detection, distance measurement, and positioning. This multi-functionality justifies the added complexity by enabling accurate positioning while also providing additional capabilities for environmental understanding and interaction
Solution Approach 2:
The patent employs cost-effective sensor solutions that balance performance and price, such as using depth sensors from commercially available smartphones or tablets, rather than requiring expensive specialized equipment. This approach maintains positioning accuracy while controlling device complexity and cost
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly improves positioning accuracy, enabling users to pinpoint specific locations, such as a parking space or lost devices, thereby enhancing user experience and meeting current user demands.
Implementation Method 1
scanning, by the lidar, the surrounding environment, and generating point cloud image data based on a scanning result
Implementation Method 2
scanning, by the lidar, the surrounding environment, and generating point cloud image data based on a scanning result
Implementation Method 3
detecting, by the depth sensor, a distance of an object in the surrounding environment, and generating depth image data based on a detecting result
Data Source
AI summary
The application provides a method, apparatus, device, storage medium and program product for target positioning. The method applied to a smart terminal includes: obtaining image data of a surrounding environment; generating a spatial image of the surrounding environment based on the image data; determining and obtaining the spatial image corresponding to a positioning target; generating anchor point information corresponding to the positioning target based on the corresponding spatial image; and determining a specific location of the positioning target using the spatial image and the anchor point information.