Visual Localization Model With 3D Training and Pose Confidence
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Solution Overview
Problem
Conventional learning-based visual localization systems fail to account for the dynamic evolution of target environments, provide partial coverage, and lack reliable confidence estimation, requiring similar sensor configurations and limiting deployment flexibility.
Innovation Solution
A neural network model trained using 3D models rendered from crowd-sourced feedback, capable of predicting pose information and confidence values, and adaptable to diverse sensor configurations through data augmentation and continuous improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If training samples are collected during specific periods of time, then the training process can be completed, but the training samples fail to reflect dynamic evolution of the target environment
Solution Approach 1:
The system transitions from static training samples collected at specific times to dynamic training samples continuously updated through crowd-sourced feedback. The neural network model is retrained periodically with new feedback data, enabling the system to adapt to environmental changes while maintaining accurate pose estimation.
Solution Approach 2:
The system implements a feedback mechanism where pose estimation results are collected from multiple users and used to continuously improve the neural network model. This feedback loop allows the system to learn from real-world variations and enhance both accuracy and adaptability over time.
2Productivity
If training samples are obtained from limited observations, then the training process is efficient, but the coverage of the target environment is partial
Solution Approach 1:
The system uses a universal neural network model architecture that can handle diverse sensor configurations and environmental conditions. By designing the model to be configuration-agnostic and training it on varied crowd-sourced data, the system achieves broad environmental coverage without requiring separate models for each scenario.
Solution Approach 2:
The system employs data augmentation techniques that modify training samples through parameter changes such as geometric transformations, lighting variations, and sensor configuration simulations. This allows the model to generalize across different conditions and expand effective coverage without collecting additional physical samples.
3Ease of operation
If conventional learning-based visual localization is used, then pose prediction can be performed, but reliable confidence estimation is not provided
Solution Approach 1:
The neural network model is segmented into multiple output heads: one for pose prediction and another for confidence estimation. This segmentation allows the model to simultaneously perform both functions with dedicated processing pathways, providing reliable confidence values alongside pose predictions.
Solution Approach 2:
The system introduces an intermediary confidence estimation module that processes the same input features as the pose prediction module but outputs reliability scores. This intermediary component bridges the gap between raw predictions and trustworthy results, enabling users to assess prediction quality.
4Stability of the object's composition
If training samples require similar sensor configurations, then training consistency is maintained, but deployment flexibility is limited
Solution Approach 1:
The system applies parameter changes through sensor configuration simulation during training. By synthetically modifying sensor parameters in training data and using domain adaptation techniques, the model learns to generalize across different sensor setups while maintaining training consistency through standardized processing pipelines.
Solution Approach 2:
The system creates virtual copies of training data with modified sensor configurations through rendering and synthesis. Instead of requiring physical samples from each sensor type, the system generates synthetic training samples that mimic various sensor characteristics, enabling flexible deployment without sacrificing training consistency.
Data Source
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AI summary
The present disclosure relates to a computing device for supporting a mobile device to perform a visual localization in a target environment. The computing device is configured to obtain and modify one or more 3D models of the target environment to generate a set of 3D models. Then, the computing device is configured to determine a set of training samples based on the set of 3D models. Each training sample comprises an image derived from the set of the 3D models and corresponding pose information. Then, the computing device is configured to train a neural network model based on the set of training samples, so that the trained neural network model is configured to predict pose information for an image captured in the target environment and to output a confidence value associated with the predicted pose information.