Localization Model Confidence Calibration for Ensemble Pose Estimation
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Solution Overview
Problem
Existing image-based localization systems face challenges in achieving consistent accuracy due to variations in image uniqueness, lighting conditions, feature presence, and differing scales across camera assemblies, leading to inconsistent and non-comparable outputs from disparate localizers.
Innovation Solution
A system employs an ensemble of calibrated localizers, using a calibration dataset to transform model-specific confidences to a standard curve, enabling coherent and harmonized pose predictions by aggregating outputs from multiple localizers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single localization model is used, then the system is simpler to manage, but it cannot consistently deliver accurate results across varying conditions
Solution Approach 1:
The system divides the localization task into multiple specialized models (ensemble of localizers), each trained to handle specific conditions or aspects of localization. This segmentation allows each model to excel at particular tasks while collectively covering all scenarios, resolving the contradiction between simplicity and consistent accuracy.
Solution Approach 2:
The system changes the parameter of model confidence by introducing calibration transformations that convert model-specific confidence scores into a standardized confidence scale. This allows multiple models with different confidence characteristics to be integrated effectively, maintaining system simplicity while improving reliability through diverse model inputs.
2Reliability
If multiple localizers are arranged and coordinated, then the system can leverage diverse strengths, but it becomes challenging to harmonize outputs from disparate localizers
Solution Approach 1:
The system implements a universal calibration framework that can handle any number of localizers with different confidence characteristics. The calibration transformation serves as a universal interface that standardizes outputs from disparate models, enabling them to be coordinated effectively without increasing coordination complexity.
Solution Approach 2:
The calibration transformation acts as an intermediary between disparate localizers and the final pose determination. It mediates the integration of confidence scores from different models by converting them to a common scale, simplifying the coordination process while maintaining the ability to leverage diverse model strengths.
3Adaptability or versatility
If disparate localizers output confidences in differing numerical ranges, then each model can be optimized independently, but direct comparability of outputs becomes infeasible
Solution Approach 1:
The system performs preliminary calibration of confidence scores before they are used for pose determination. By applying calibration transformations in advance, the system converts model-specific confidence ranges into a standardized scale, enabling direct comparability while preserving the flexibility of independent model optimization.
Solution Approach 2:
The calibration transformation serves as an intermediary layer between models with different confidence ranges and the confidence comparison process. It translates confidences from various numerical ranges into a unified scale, maintaining model optimization flexibility while ensuring precise comparability of outputs.
Data Source
AI summary
A system performs image-based localization with an ensemble of localizers. The system receives a target frame from image data captured by a camera assembly of a client device. The system deploys an ensemble of localizers, each disparately trained to output a pose of the target frame and a model-specific confidence for the pose. The system calibrates each model-specific confidence by applying a model-specific calibration transformation to transform the model-specific confidence to a calibrated confidence. The system determines a final pose for the target frame by aggregating the poses output by the ensemble based on the calibrated confidences. The system may provide a visual positioning service (VPS) with the image-based localization. The system may also leverage the image-based localization to generate augmented reality content for presentation to a user.