Unified Visual Localization Architecture for Pose Determination
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Solution Overview
Problem
Existing navigation systems face challenges in determining the position and attitude of vehicles efficiently, as they rely on multiple sensors and paradigms that are not universally applicable across different operational contexts.
Innovation Solution
A unified visual localization architecture that includes an image acquisition device, a memory device for storing an image database, and a processor to identify matching data, select a suitable vision localization paradigm, and determine the object's pose using the query frame and lens characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors and paradigms are used to determine position and attitude, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent implements a unified visual localization architecture where a single image acquisition device and processing system can operate across multiple different environments and operational contexts by selecting from multiple vision localization paradigms. This multi-functional approach eliminates the need for separate specialized sensors for each paradigm while maintaining high measurement precision through paradigm-specific processing algorithms.
2Adaptability or versatility
If multiple vision localization paradigms are integrated, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent segments the vision localization system into distinct paradigms (e.g., image-based, map-based, landmark-based) that can be independently selected and executed. Each paradigm is processed through a common frontend that handles image acquisition and preprocessing, allowing the system to maintain high adaptability to different operational contexts while managing complexity through modular paradigm implementation.
3Measurement precision
If comprehensive sensor data is processed, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent applies partial action by selecting and executing only the necessary vision localization paradigm based on the specific operational context and available data. Rather than processing all possible sensor data and paradigms simultaneously, the system identifies the most appropriate paradigm (e.g., using image-based localization when GPS is unavailable) to achieve sufficient measurement precision with reduced processing time and computational resources.
Data Source
AI summary
Systems and methods for providing a unified visual localization architecture are described herein. In some implementations, a system includes an image acquisition device mounted to an object, the image acquisition device configured to acquire a query frame of an environment containing the object. The system also includes a memory device configured to store an image database. Further, the system includes at least one processor configured to execute computer-readable instructions that direct the at least one processor to identify a set of data in the image database that potentially matches the query frame; identify a vision localization paradigm in a plurality of vision localization paradigms; and determine a pose for the object using the set of data, the query frame, and lens characteristics for the image acquisition device as inputs to the vision localization paradigm.