Two-Stream Network for Self-Supervised Ego-Motion Estimation
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Solution Overview
Problem
Current methods for ego-motion estimation in autonomous vehicles rely on expensive LIDAR sensors, and existing deep learning approaches for monocular 3D reconstruction lack a complete solution for self-supervised learning, particularly in network architecture and training, limiting their effectiveness in ill-posed and under-determined regimes.
Innovation Solution
A self-supervised system using a two-stream network architecture processes RGB and depth streams to learn ego-motion estimation, incorporating a depth module for depth estimation and a pose module for six degrees-of-freedom transformation, with aggressive image augmentation to prevent overfitting and induce sparsity in feature learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR sensors are used for ego-motion determination, then measurement precision and reliability are improved, but cost increases significantly
Solution Approach 1:
The patent replaces expensive LIDAR sensors with inexpensive monocular camera sensors for ego-motion estimation. The camera-based monocular depth estimation system uses affordable imaging components rather than costly active sensing hardware, making the technology economically viable for global scale deployments while maintaining functional capability through sophisticated algorithms.
Solution Approach 2:
The patent substitutes the mechanical LIDAR sensing system with an optical camera-based system combined with deep learning algorithms. Instead of using physical laser ranging, the system uses monocular vision with self-supervised learning to achieve ego-motion estimation, replacing mechanical sensing with computational approaches.
2Adaptability or versatility
If deep learning approaches are used for monocular 3D reconstruction, then reliance on hand-engineered features is reduced, but training complexity and data requirements increase
Solution Approach 1:
The patent implements self-supervised learning where the system learns to estimate depth and ego-motion from unlabeled monocular images without requiring manually annotated ground truth data. The network uses the images themselves as supervision signals, automatically generating its own training labels through geometric constraints and photometric consistency, thereby serving its own training needs without external intervention.
Solution Approach 2:
The patent inverts the traditional supervised learning approach by eliminating the need for labeled training data. Instead of using ground truth depth and motion labels to train the network, the system learns from unlabeled images by enforcing geometric and photometric consistency constraints, turning the unlabeled data into the training signal itself.
3Loss of time
If self-supervised learning is used for depth and pose estimation, then labeling burden is reduced, but network architecture and training effectiveness need significant improvement
Solution Approach 1:
The patent divides the ego-motion estimation task into two separate neural network streams: a depth estimation network that predicts per-pixel depth values, and a pose estimation network that predicts camera motion parameters. This segmentation allows each network to specialize in its specific function and be trained independently using self-supervised signals, improving overall system reliability while maintaining the benefits of self-supervised learning.
Solution Approach 2:
The patent introduces depth estimation as an intermediary component that bridges monocular images and ego-motion estimation. The depth network serves as a mediator that provides geometric information to the pose estimation network, enabling more accurate self-supervised learning of camera motion without requiring labeled data, thereby improving reliability while maintaining the self-supervised approach.
Data Source
AI summary
System, methods, and other embodiments described herein relate to estimating ego-motion. In one embodiment, a method for estimating ego-motion based on a plurality of input images in a self-supervised system includes receiving a source image and a target image, determining a depth estimation Dt based on the target image, determining a depth estimation Ds based on a source image, and determining an ego-motion estimation in a form of a six degrees-of-freedom (6 DOF) transformation between the target image and the source image by inputting the depth estimations (Dt, Ds), the target image, and the source image into a two-stream network architecture trained to output the 6 DOF transformation based at least in part on the depth estimations (Dt, Ds), the target image, and the source image.