Multi-Camera Loop Closure Detection for Cross-Directional Mapping
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Solution Overview
Problem
Existing loop closure detection methods in mobile devices, such as robots and unmanned aerial vehicles, require consistent path and direction for accurate results, leading to inefficient mapping due to repetitive paths in the same direction.
Innovation Solution
A method involving multiple cameras installed on a mobile device to collect images in different detection directions simultaneously, with a main thread and sub-threads performing loop closure detection based on feature information, allowing for cross-directional detection without requiring consistent paths, thereby improving mapping efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the mobile device requires consistent path and direction for loop closure detection, then the detection accuracy is improved, but the mapping efficiency deteriorates due to repetitive paths
Solution Approach 1:
The patent applies dimensionality change by introducing multiple detection directions (dimensions) for loop closure detection. Instead of detecting loop closures in a single direction, the system uses multiple cameras to capture images from different directions simultaneously, allowing the mobile device to detect loop closures regardless of the direction of travel. This resolves the contradiction by enabling accurate detection without requiring repetitive paths in the same direction.
Solution Approach 2:
The patent implements universality by creating a multi-directional detection system that can handle various detection scenarios. The main thread and sub-threads perform loop closure detection based on feature information from multiple cameras, making the system universally applicable to different detection directions and paths. This allows the system to maintain high detection accuracy while improving mapping efficiency by eliminating the need for repetitive unidirectional paths.
2Measurement precision
If the mobile device takes many repetitive paths in the same direction during mapping, then the loop closure detection accuracy is improved, but the mapping time increases
Solution Approach 1:
The patent resolves this time-loss contradiction by adding directional dimensions to the detection system. Multiple cameras capture images from different directions simultaneously, allowing the system to detect loop closures in various directions without requiring multiple repetitive passes in the same direction. This significantly reduces the mapping time while maintaining detection accuracy.
Solution Approach 2:
The patent applies continuity of useful action by enabling continuous loop closure detection across multiple directions simultaneously. The main thread and sub-threads process feature information from multiple cameras in parallel, ensuring that detection actions continue without interruption across different directions. This eliminates the need for repetitive unidirectional paths, reducing mapping time while maintaining continuous accurate detection.
3Device complexity
If the mobile device uses single-direction detection, then the system complexity is reduced, but the mapping quality deteriorates
Solution Approach 1:
The patent resolves this quality-complexity contradiction by introducing multiple detection directions through multiple cameras. While this increases system complexity, it simultaneously improves mapping quality by enabling loop closure detection from various angles. The structured approach with main threads and sub-threads manages this complexity systematically, achieving high-quality mapping that would be impossible with single-direction detection.
Data Source
AI summary
A loop closure detection method, a mobile device, and a computer readable storage medium are provided. The method includes: collecting images in different detection directions simultaneously through C0 cameras installed on the mobile device to obtain an image data group comprising C0 images; calculating feature information of each image in the image data group; performing a loop closure detection in C0 sub-threads respectively based on the feature information to obtain a loop closure detection result of each sub-thread; and determining a loop closure detection result of the mobile device based on the loop closure detection result of each sub-thread. In this manner, cross detections in a plurality of detection directions can be realized, which breaks through the limitation of loop closure detection in the prior art with respect to path direction, avoids repeated paths in the same direction, and greatly improves the mapping efficiency.