Global Localization Model Domain Gap Reduction
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Solution Overview
Problem
Existing global localization methods face challenges in accurately estimating the pose of a device in non-static environments due to the domain gap between synthetic and real images, leading to poor performance with real images.
Innovation Solution
The method involves training a global localization model using a first generative adversarial network (GAN) loss that compares features extracted from synthetic and real images, and a second GAN loss that compares coordinate maps estimated from these features, to bridge the domain gap and improve pose estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If training is performed with synthetic data, then the trained network exhibits high performance for synthetic images, but has extremely low performance for real images due to domain gap
Solution Approach 1:
The patent introduces a domain adaptation layer as an intermediary component between the feature extraction network and the pose estimation module. This layer learns to transform features from different domains (synthetic and real images) into a unified representation space, enabling the network to handle both domains effectively. The domain adaptation layer acts as a mediator that bridges the domain gap, allowing the model trained on synthetic data to generalize well to real images.
Solution Approach 2:
The patent employs parameter adaptation techniques where the network parameters (weights and biases) are dynamically adjusted based on the input domain. During training, the model learns domain-specific parameter transformations that allow it to adapt its behavior when processing real images versus synthetic images. This parameter flexibility enables the model to maintain high pose estimation accuracy across different image domains without requiring separate models.
2Measurement precision
If training is performed with real data, then pose accuracy can be increased, but it is difficult to acquire labeled real data
Solution Approach 1:
The patent creates synthetic copies of real-world scenes with known ground truth pose information. By generating realistic synthetic images from 3D models or rendered environments, the system obtains large amounts of labeled training data without the difficulty of manual annotation. These synthetic copies preserve the essential visual characteristics of real images while providing accurate pose labels, solving the data acquisition problem.
Solution Approach 2:
The patent performs preliminary data preparation by pre-processing and pre-training the network on synthetic data before deploying it for real image processing. This preliminary training phase allows the model to learn fundamental pose estimation patterns from synthetically generated labeled data, establishing a strong foundation that can later be fine-tuned or adapted for real images without requiring extensive real labeled data.
3Reliability
If Structure from Motion (SfM) is used to acquire map and ground truth pose, then mapping can be created, but accurate data is difficult to acquire and localization performance is greatly degraded if inaccurate pose is used
Solution Approach 1:
The patent replaces traditional SfM (Structure from Motion) algorithms with a deep learning-based pose estimation system. Instead of relying on complex geometric computations and iterative optimization inherent in SfM, the system uses trained neural networks to directly estimate pose from images. This substitution eliminates the accumulation of errors that plagues SfM methods and provides more reliable and consistent pose estimates, especially in challenging environments.
Solution Approach 2:
The patent introduces a learned feature representation as an intermediary between the raw image input and the final pose estimation. This intermediate feature space, extracted by the trained network, serves as a robust bridge that captures essential geometric and semantic information while being invariant to many disturbances. This intermediary representation makes the pose estimation more reliable compared to direct SfM approaches that operate on raw pixel data.
Data Source
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AI summary
A method with global localization includes: extracting a feature by applying an input image to a first network; estimating a coordinate map corresponding to the input image by applying the extracted feature to a second network; and estimating a pose corresponding to the input image based on the estimated coordinate map, wherein either one or both of the first network and the second network is trained based on either one or both of: a first generative adversarial network (GAN) loss determined based on a first feature extracted by the first network based on a synthetic image determined by three-dimensional (3D) map data and a second feature extracted by the first network based on a real image; and a second GAN loss determined based on a first coordinate map estimated by the second network based on the first feature and a second coordinate map estimated by the second network based on the second feature.