SLAM Keyframe Pruning Using Pose Quantization Grids
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Solution Overview
Problem
Conventional SLAM algorithms face challenges in managing large volumes of keyframe data, leading to increased computational load and memory usage, with techniques like the sliding window approach potentially losing valuable environmental context and introducing map inconsistencies.
Innovation Solution
Pose quantization-based keyframe pruning, which organizes keyframes into a virtual pose grid and selectively retains keyframes based on their contribution to the environmental map, reducing redundancy and optimizing memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional SLAM algorithms store all keyframe data, then the environmental map completeness is improved, but the memory usage and computational load increase significantly
Solution Approach 1:
The patent applies parameter changes by quantizing pose data from continuous to discrete representations. By organizing keyframes into a virtual pose grid with quantized spatial indices and orientation components, the system transforms the data structure to enable efficient pruning while preserving essential environmental information. This parameter transformation allows the system to maintain map completeness with reduced memory requirements.
Solution Approach 2:
The patent implements discarding and recovering by selectively pruning redundant keyframes based on quantized pose data analysis. The system identifies and removes duplicate or unnecessary keyframes from memory while preserving those that contribute unique environmental information. This selective discarding reduces memory usage while the recovery of essential keyframes maintains the completeness and accuracy of the environmental map.
2Measurement precision
If conventional SLAM algorithms process all keyframe data, then the mapping accuracy is improved, but the processing time and computational load increase
Solution Approach 1:
The patent transforms pose data parameters from continuous to quantized discrete values, enabling efficient comparison and pruning of redundant keyframes. By organizing data into a virtual pose grid with quantized spatial and orientation parameters, the system can quickly identify and remove duplicate keyframes without compromising mapping accuracy, thereby significantly improving processing efficiency.
Solution Approach 2:
The patent extracts and removes redundant keyframes from the dataset by analyzing quantized pose data. The system identifies keyframes that provide duplicate or unnecessary information and extracts only the essential, non-redundant keyframes for further processing. This extraction process reduces computational load while maintaining mapping accuracy by focusing processing resources on informative keyframes only.
3Quantity of substance
If conventional SLAM algorithms discard older keyframe data, then the memory usage is reduced, but the map consistency deteriorates
Solution Approach 1:
The patent applies parameter changes by quantizing pose data into discrete spatial indices and orientation components within a virtual pose grid. This transformation enables the system to objectively evaluate keyframe uniqueness and identify redundant data across the entire dataset, not just based on age. The quantized parameters allow for consistent comparison and pruning while preserving keyframes that contribute to map consistency regardless of their temporal position.
Solution Approach 2:
The patent performs preliminary organization of keyframes into a virtual pose grid with quantized parameters before pruning. By pre-organizing the data structure and computing quantized spatial indices and orientation components in advance, the system can efficiently evaluate all keyframes for redundancy and make informed decisions about which to retain. This preliminary action ensures that important keyframes are preserved while removing only truly redundant data, maintaining map consistency with reduced memory usage.
Data Source
AI summary
Embodiments of the present invention relate to techniques for managing keyframe data in a Simultaneous Localization and Mapping (SLAM) system of an Augmented Reality (AR) device. The method involves obtaining a plurality of keyframes, each linked to pose data comprising spatial and orientation data derived from raw data captured by sensors. The pose data for each keyframe is quantized according to predefined parameters, creating a structured pose grid of quantized cells. The technique includes analyzing the quantized pose data to identify excess keyframes that exceed a predetermined threshold within these cells. Redundant keyframes are pruned from memory, optimizing the SLAM system's efficiency by reducing computational load and memory usage. This selective pruning process ensures that the AR device retains a comprehensive and accurate environmental map while operating within the constraints of limited system resources.