Unified Visual Localization Architecture for Adaptive Pose Estimation
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Solution Overview
Problem
Existing navigation systems face challenges in determining the precise position and attitude of vehicles due to the limitations of current sensor technologies and the need for unified visual localization architectures that can adapt to various operational contexts.
Innovation Solution
A unified visual localization architecture that includes an image acquisition device, a memory device for storing an image database, and at least one processor to identify matching data, select a suitable vision localization paradigm, and determine the pose of the object using the query frame and lens characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple vision localization paradigms are implemented to improve accuracy and adaptability, then the system can better handle various operational contexts, but the device complexity increases
Solution Approach 1:
The system segments the vision localization task into multiple distinct paradigms (e.g., feature-based, direct methods, optical flow-based approaches), each optimized for specific operational contexts. The processor selectively activates only the relevant paradigm based on current conditions, managing complexity through modular organization rather than monolithic design.
Solution Approach 2:
The system dynamically selects and switches between different vision localization paradigms based on real-time operational context assessment. This dynamic adaptation allows the system to optimize performance for each situation while maintaining manageable complexity through on-demand activation rather than simultaneous operation of all paradigms.
2Measurement precision
If precise pose determination is achieved through multiple vision localization paradigms, then measurement precision improves, but computational time and processing complexity increase
Solution Approach 1:
The system performs preliminary assessment of the operational context to determine which vision localization paradigm is most appropriate before executing the full computation. This preliminary action filters out unsuitable paradigms in advance, preventing wasteful computational time on inappropriate methods while ensuring the selected paradigm provides the necessary precision.
Solution Approach 2:
The system changes computational parameters by selecting different algorithms and processing depths based on the operational context. For straightforward scenarios, simpler paradigms with lower computational requirements are used, while complex scenarios trigger more sophisticated paradigms, thus optimizing the balance between precision and computational time.
3Adaptability or versatility
If a unified visual localization architecture is implemented to enhance adaptability, then the system can handle diverse environments, but the device complexity and implementation difficulty increase
Solution Approach 1:
The unified visual localization architecture implements a universal processor framework capable of executing multiple vision localization paradigms through a common interface. This multi-functionality allows the system to handle diverse environments with a single unified architecture rather than requiring separate specialized systems for each environment type.
Solution Approach 2:
The system introduces an intermediary operational context assessment mechanism that mediates between the diverse environmental inputs and the multiple vision localization paradigms. This intermediary layer translates various environmental conditions into standardized paradigm selection criteria, simplifying the integration of multiple paradigms into the unified architecture.
Data Source
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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.