Visual Localization Training With Synthetic Images for Extreme Domain Shifts
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Solution Overview
Problem
Visual localization systems in autonomous robots face challenges in accurately estimating camera pose due to extreme domain shifts caused by changes in lighting conditions, occlusions, weather, and seasonal variations, leading to unreliable navigation.
Innovation Solution
Utilize generative AI models to generate synthetic images simulating domain shifts, validate these images using geometric filters, and enhance the training of image retrieval models with a contrastive loss function and domain randomization to improve resilience to environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If visual localization uses pure visual information from camera images, then the system can operate autonomously without external sensors, but the system becomes sensitive to environmental changes such as lighting conditions, occlusions, weather, and seasonal variations
Solution Approach 1:
The patent applies preliminary action by pre-training the image retrieval model using synthetic images that simulate various environmental conditions (different lighting, weather, seasons, occlusions) before deployment. This pre-training prepares the model to handle domain shifts in real-world scenarios, improving reliability while maintaining autonomous operation without external sensors.
2Measurement precision
If the image retrieval model is trained only on real images from the environment, then the training data is authentic, but the model fails to generalize under extreme domain shifts and environmental variations
Solution Approach 1:
The patent uses copying by generating synthetic images that replicate real environmental scenes under various conditions (different lighting, weather, seasons). These synthetic copies are used to augment the training dataset, enabling the model to learn invariant features and generalize better to domain shifts while maintaining localization precision on real images.
Solution Approach 2:
The patent applies parameter changes by systematically varying environmental parameters in synthetic image generation (lighting conditions, weather patterns, seasonal changes, occlusion levels). This creates a diverse training dataset that teaches the model to maintain localization accuracy across different parameter configurations and domain shifts.
3Reliability
If synthetic images are generated to augment training data, then the model's robustness to domain shifts improves, but the training process becomes more complex involving generative models and validation
Solution Approach 1:
The patent uses an intermediary approach by introducing a validation mechanism that checks geometric consistency between synthetic and real images. This intermediary validation step ensures that only high-quality synthetic images that preserve geometric relationships are used for training, managing complexity while maintaining reliability improvements.
4Manufacturing precision
If geometric validation is applied to synthetic images, then the quality and consistency of training data improves, but the validation process requires additional computational resources and time
Solution Approach 1:
The patent applies local quality by performing geometric validation selectively on critical regions of synthetic images rather than uniformly across all images. This focuses computational resources on validating the most important geometric relationships while reducing overall validation time and processing requirements.
Data Source
AI summary
The system and method to improve image retrieval models in case of extreme domain shifts for long term visual localization. An initial dataset of real images of an environment can be obtained from an image database that may comprise of matching pairs and sets of non-matching images. The initial dataset is augmented with synthetic variants of each image to represent the potential changing conditions of the environment. The synthetic variants may be generated by using generative AI models. To validate synthetic images, a geometric consistency score is calculated for each synthetic pair based on a degree of geometric correspondence between images of the synthetic pair. A subset of k synthetic tuples is selected from a set of tuples corresponding to a matching pair, either randomly or based on the geometric consistency score, to compute a contrastive loss function for training of image retrieval model along with other custom/disclosed settings.