Attention-Based Feature Compression for Autonomous Localization
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Solution Overview
Problem
Current methods for localizing objects in dynamic environments, such as autonomous vehicles, face challenges in efficiently processing and storing large amounts of data for accurate object localization and mapping, leading to increased computational resources and storage needs.
Innovation Solution
The use of machine-learned feature extraction models, combined with lossless compression operations like Huffman coding and run-length encoding, to generate compressed feature representations of environments, allowing for efficient storage and real-time localization of objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full-resolution feature representations are stored and processed, then localization accuracy is improved, but storage requirements and computational resources increase
Solution Approach 1:
The patent extracts only the most relevant and discriminative features from the environment data using machine-learned feature extraction models. Instead of storing and processing complete high-resolution feature representations, the system identifies and retains key features that are sufficient for accurate localization, thereby reducing storage requirements while maintaining localization accuracy.
Solution Approach 2:
The patent transforms the feature representation parameters from high-resolution complete data to compressed feature vectors through machine learning models. This parameter transformation reduces the dimensionality and storage requirements of the feature representations while preserving the essential information needed for localization tasks.
2Quantity of substance
If compressed feature representations are used, then storage requirements are reduced, but processing speed and localization accuracy may deteriorate
Solution Approach 1:
The patent performs feature extraction and compression in advance using machine-learned models trained on large datasets. The compressed feature representations are pre-processed and stored in an optimized format, enabling fast retrieval and processing during actual localization operations without compromising accuracy or speed.
Solution Approach 2:
The patent replaces traditional mechanical data processing approaches with machine learning-based feature extraction and compression. This substitution enables efficient processing of compressed representations by leveraging learned patterns and relationships, maintaining high processing speed while working with reduced data sizes.
3Measurement precision
If machine-learned feature extraction models are applied, then feature representation quality is improved, but computational complexity increases
Solution Approach 1:
The patent segments the feature extraction process into distinct machine learning model components that operate in sequence. Each model layer processes specific aspects of the data and produces intermediate features, allowing for modular computation and optimization. This segmentation reduces overall computational complexity while maintaining high feature representation quality.
4Reliability
If lossless compression operations are used, then data integrity is maintained, but compression ratio and storage efficiency are reduced
Solution Approach 1:
The patent creates compressed feature vector copies that capture the essential information from the original environment data. These compressed representations serve as sufficient copies for localization tasks, maintaining data integrity where needed while achieving significant storage efficiency improvements through selective feature retention rather than complete data preservation.
Data Source
AI summary
Systems, methods, tangible non-transitory computer-readable media, and devices associated with object localization and generation of compressed feature representations are provided. For example, a computing system can access training data including a target feature representation and a source feature representation. An attention feature representation can be generated based on the target feature representation and a machine-learned attention model. An attended target feature representation can be generated based on masking the target feature representation with the attention feature representation. A matching score for the source feature representation and the target feature representation can be determined. A loss associated with the matching score and a ground-truth matching score for the source feature representation and the target feature representation can be determined. Furthermore, parameters of the machine-learned attention model can be adjusted based on the loss.