SLAM Localization Using Gravitational Vertical Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computer vision systems face challenges in localizing and mapping without the use of AR markers, especially in scenarios where large or inaccessible objects are being augmented, as they require advance indication of camera position and object positions within the environment.
Innovation Solution
The implementation of simultaneous localization and mapping (SLAM) techniques, which iteratively build a map of the environment and determine the camera's position, using image processing and tracking methods to derive camera poses and landmark positions without the need for AR markers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AR markers are used to assist in localization and mapping, then measurement precision and orientation detection are improved, but device complexity and ease of operation deteriorate due to the need to carry and position physical markers
Solution Approach 1:
The patent extracts the localization function from physical AR markers and implements it through natural feature detection in the environment. The system detects and tracks features such as corners, edges, and textures of real-world objects directly, eliminating the need for separate marker components while maintaining measurement precision for camera positioning and orientation.
Solution Approach 2:
The patent makes the camera system universal by enabling it to perform both standard imaging functions and localization functions simultaneously. The same camera captures both the visual scene for AR rendering and the feature points needed for SLAM, eliminating the need for separate marker detection hardware and simplifying user operation.
2Manufacturing precision
If AR markers are used for scale indication, then manufacturing precision of scale measurement is improved, but loss of time increases due to the need to position markers before gameplay
Solution Approach 1:
The patent performs preliminary action by pre-detecting and storing three-dimensional positions of environmental features during the SLAM mapping phase. This pre-established spatial map allows for immediate scale calculation during AR rendering without requiring real-time marker positioning, eliminating setup time while maintaining measurement accuracy.
Solution Approach 2:
The system makes the environment self-service for scale measurement by using naturally occurring features of objects (such as their inherent size and shape) as reference points. The system automatically detects and utilizes these features without requiring users to attach or position any external measurement aids, enabling immediate gameplay without setup.
3Adaptability or versatility
If simultaneous localization and mapping is implemented without AR markers, then adaptability to different environments is improved, but device complexity increases due to iterative processing requirements
Solution Approach 1:
The patent segments the complex SLAM processing into distinct functional modules: feature detection, feature tracking, map building, and camera pose estimation. This modular segmentation allows the system to handle different environment types independently while managing processing complexity through organized, reusable components that can be selectively activated.
Data Source
AI summary
A method generates a three-dimensional map of a region from successive images of that region captured from different camera poses. The method captures successive images of the region, detects a gravitational vertical direction in respect of each captured image, detects feature points within the captured images and designates a subset of the captured images as a set of keyframes each having respective sets of image position data representing image positions of landmark points detected as feature points in that image. The method also includes, for a captured image (i) deriving a camera pose from detected feature points in the image; (ii) rotating the gravitational vertical direction to the coordinates of a reference keyframe using the camera poses derived for that image and the reference keyframe; and (iii) comparing the rotated direction with the actual gravitational vertical direction for the reference keyframe to detect a quality measure of that image.