Multi-Camera SLAM Localization via Geometric Constraints
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current augmented reality systems for mobile devices face challenges in accurately performing localization and mapping, particularly in determining the position, orientation, and tracking of features within environments, especially when faced with varying image quality and non-overlapping camera views.
Innovation Solution
The system employs multiple cameras and sensors, such as front-facing and rear-facing cameras, along with orientation sensors, to capture and evaluate images, designate primary and secondary images based on quality, and apply geometric constraints to perform simultaneous localization and mapping (SLAM), enabling robust tracking and mapping of horizontal and vertical surfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras and sensors are used for localization and mapping, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
The system divides the localization and mapping function into separate modules: a tracking module that identifies geometric constraints and processes images, and a SLAM subsystem that performs the actual localization and mapping. This segmentation allows each module to specialize in specific tasks, improving overall precision while managing complexity through modular design.
Solution Approach 2:
The patent combines multiple cameras and sensors (front-facing camera, rear-facing camera, orientation sensors) into a unified multi-sensor system that works together for localization and mapping. By merging these sensors and processing their data through the tracking module and SLAM subsystem, the system achieves improved measurement precision through sensor fusion while managing complexity through integrated processing.
2Adaptability or versatility
If multiple cameras with non-overlapping views are used, then adaptability to different environments is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The tracking module serves as an intermediary between the multiple cameras with non-overlapping views and the SLAM subsystem. It identifies geometric constraints in the environment and processes images from different cameras to extract features, bridging the gap between non-overlapping views and making the data usable for accurate localization and mapping.
Solution Approach 2:
The system changes parameters by evaluating image quality and designating either the front-facing or rear-facing camera as the primary image source based on current conditions. This dynamic parameter adjustment allows the system to adapt to different environmental conditions and maintain tracking accuracy despite non-overlapping camera views.
3Measurement precision
If image quality evaluation and primary/secondary designation are implemented, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs partial evaluation by designating one image as primary and another as secondary based on quality metrics, rather than fully processing all possible images. This partial action approach improves measurement precision by selecting the best image data while reducing processing time by avoiding exhaustive analysis of all images.
Data Source
AI summary
Systems and methods for performing localization and mapping with a mobile device are disclosed. In one embodiment, a method for performing localization and mapping with a mobile device includes identifying geometric constraints associated with a current area at which the mobile device is located, obtaining at least one image of the current area captured by at least a first camera of the mobile device, obtaining data associated with the current area via at least one of a second camera of the mobile device or a sensor of the mobile device, and performing localization and mapping for the current area by applying the geometric constraints and the data associated with the current area to the at least one image.