Dynamic SLAM Algorithm Selection via SNR Metrics
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Solution Overview
Problem
Existing virtual, augmented, and mixed reality (xR) systems face challenges in efficiently handling multiple Simultaneous Localization and Mapping (SLAM) sources and algorithms, leading to accuracy and tracking performance issues due to varying user movements and environmental conditions.
Innovation Solution
The system employs a processor-based Information Handling System (IHS) that applies and selects between different SLAM algorithms (such as EKF, Monte Carlo, and MAP) and camera sources (like World-Facing Camera, Gesture Recognition, and IR cameras) based on Signal-to-Noise (SNR) metrics, dynamically switching between them to optimize SLAM performance and reduce computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple SLAM algorithms are applied to evaluate performance under varying conditions, then SLAM accuracy is improved, but computational load increases
Solution Approach 1:
The system dynamically selects SLAM algorithms and camera sources based on real-time SNR metrics and environmental conditions. Instead of running all algorithms continuously, the system adapts its computational approach by switching between algorithms (e.g., EKF, Monte Carlo, MAP) and camera sources (WFC, GRT, IR, NIR) according to current performance metrics, thereby maintaining high accuracy while reducing unnecessary computational load.
Solution Approach 2:
The system changes operational parameters by selecting different SLAM algorithms and camera sources based on SNR metrics. When SNR is high, more computationally intensive algorithms may be selected; when SNR is low, lighter algorithms are chosen. This parameter-based selection allows the system to optimize the balance between accuracy and computational load according to real-time conditions.
2Reliability
If multiple camera sources are processed simultaneously, then tracking performance is improved, but device complexity increases
Solution Approach 1:
The system dynamically selects which camera sources to process based on real-time SNR metrics and environmental conditions. Instead of continuously processing all camera sources (WFC, GRT, IR, NIR), the system adapts by activating only the most suitable camera source for current conditions, thereby maintaining reliable tracking performance while reducing system complexity and processing overhead.
Solution Approach 2:
The system changes the operational state by selecting different camera sources based on SNR metrics. When certain camera sources provide sufficient SNR, they are prioritized; when SNR is insufficient, alternative camera sources are selected. This dynamic parameter selection maintains tracking reliability while avoiding the complexity of processing all camera sources simultaneously.
3Adaptability or versatility
If SLAM algorithms are dynamically selected based on SNR metrics, then adaptability is improved, but processing time increases
Solution Approach 1:
The system performs preliminary evaluation of SNR metrics for different SLAM algorithms and camera sources to determine the optimal combination before actual SLAM processing. By pre-assessing the quality of input data from different sources and algorithms, the system can quickly select the best combination without extensive trial-and-error processing, thereby maintaining high adaptability while minimizing additional processing time.
4Use of energy by moving object
If computational resources are minimized, then energy efficiency is improved, but SLAM accuracy deteriorates
Solution Approach 1:
The system changes computational parameters by selecting SLAM algorithms and camera sources based on SNR metrics. When energy efficiency is prioritized, the system selects lighter algorithms and fewer camera sources while ensuring SNR thresholds are met to maintain acceptable accuracy. When higher accuracy is required, the system can allocate more computational resources. This dynamic parameter adjustment optimizes the trade-off between energy consumption and SLAM accuracy based on real-time conditions.
Data Source
AI summary
Embodiments of systems and methods for handling multiple Simultaneous Localization and Mapping (SLAM) sources and algorithms in virtual, augmented, and mixed reality (xR) applications are described. In an embodiment, an Information Handling System (IHS) may apply a first SLAM algorithm to first SLAM data captured via a first camera source mounted on a Head-Mounted Device (HMD) coupled to the IHS to produce a first Signal-to-Noise (SNR) metric; apply a second SLAM algorithm to the first SLAM data to produce a second SNR metric; select: (i) the first SLAM algorithm in response to the first SNR metric being greater than the second SNR metric, or (ii) the second SLAM algorithm in response to the second SNR metric being greater than the first SNR metric; and produce a map of a space where the HMD is located, at least in part, by applying the selected SLAM algorithm to subsequently captured SLAM data.