AR Device Eye Tracking SLAM Point Cloud Update
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Solution Overview
Problem
Existing Augmented Reality (AR) devices face computational burdens due to the afterimage problem in Simultaneous Localization and Mapping (SLAM) algorithms, especially in dynamic environments, which requires extensive computations for feature point detection and map updates.
Innovation Solution
An image processing method that uses eyeball tracking to determine a binocular watching range in a three-dimensional space, employing a sight watching model to quickly update environment map point cloud data within the watching range, thereby reducing computational load and eliminating afterimage effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the SLAM algorithm is used for spatial localization and environment mapping, then the AR device can achieve real-time interaction and environment mapping, but it forms false afterimage in scenes with moving objects and increases computational burden
Solution Approach 1:
The patent segments the environment map update process by identifying and excluding moving objects from the mapping process. The system divides the scene into static background regions and dynamic foreground regions, applying SLAM only to static regions to eliminate afterimage effects while reducing unnecessary computations on moving objects
Solution Approach 2:
The patent applies partial action by performing environment mapping only in regions where it is necessary and effective. By detecting moving objects and excluding them from the mapping process, the system performs mapping operations only on static regions, reducing overall computational burden while maintaining mapping accuracy in relevant areas
2Reliability
If improved solutions are applied to address the afterimage problem of the SLAM algorithm, then the afterimage effect is reduced, but the computational intensity and time consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by performing moving object detection before the environment map update process. By identifying moving objects in advance and marking them for exclusion, the system prepares the data structure to efficiently skip these regions during subsequent mapping operations, avoiding the need for complex post-processing to remove afterimages
Data Source
AI summary
An image processing method for an augmented reality device, an augmented reality device and a storage medium are provided. The image processing method for the augmented reality device includes: tracking lines of sight of a left eye and a right eye to determine a position of a binocular watching range in a three-dimensional space; shooting an image of an external environment where the augmented reality (AR) device is located, and acquiring environment map point cloud data based on a Simultaneous Localization and Mapping (SLAM) algorithm; and updating the environment map point cloud data within the binocular watching range in response to detecting an update of the image of the external environment.