Neural Camera Models for Self-Supervised Visual Odometry
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Solution Overview
Problem
Existing visual odometry systems face challenges in accurately determining ego-vehicle motion and 3D environment mapping, particularly when using monocular cameras, due to limitations in depth perception and the need for known camera models.
Innovation Solution
The implementation of a neural camera model that enables self-supervised learning for visual odometry, allowing keypoint lifting and projection without requiring knowledge of the camera model, and combining this with keypoint networks to estimate correspondences and transformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional pinhole camera models are used for visual odometry, then the system can achieve closed-form lifting and projection operations, but the system cannot accurately handle complex lens distortions such as fisheye and catadioptric lenses
Solution Approach 1:
The patent changes the parameters of the camera model by introducing learnable parameters that adapt the projection model to different camera geometries. Instead of using fixed pinhole model parameters, the system learns camera-specific parameters through self-supervised training, enabling accurate handling of various lens types including fisheye and catadioptric lenses while maintaining the benefits of parametric models.
Solution Approach 2:
The patent makes the camera model dynamic by allowing it to adapt and change based on the specific camera being used. The system dynamically adjusts the projection model through self-supervised learning, transforming a static pinhole model into a flexible framework that can accommodate different camera geometries without requiring manual recalibration or model switching.
2Adaptability or versatility
If parametric distortion models are used to handle complex lens designs, then the system can accommodate various camera types, but the models become significantly more complex and difficult to calibrate
Solution Approach 1:
The patent implements self-service by enabling the camera model to automatically adapt to different camera geometries through self-supervised learning. The system calibrates itself without external intervention or manual parameter tuning, eliminating the complexity of traditional calibration procedures while maintaining adaptability to various lens types.
Solution Approach 2:
The patent replaces the mechanical calibration process with a learning-based approach. Instead of manually adjusting camera parameters through complex calibration procedures, the system uses neural networks to automatically learn and adapt to camera characteristics, substituting a complex mechanical calibration system with a more efficient software-based solution.
3Measurement precision
If specialized architectures are created for specific camera models, then the system can be optimized for that camera type, but the architecture must be significantly modified before it can be used with novel cameras
Solution Approach 1:
The patent creates a universal camera model that can handle multiple camera types through a single unified architecture. The self-supervised learning framework enables the same network to adapt to different camera geometries, eliminating the need for specialized architectures for each camera type while maintaining optimization for specific models through learned parameters.
4Ease of manufacture
If monocular cameras are used for depth estimation, then the system benefits from cost-effectiveness, but the depth perception accuracy is limited compared to LiDAR or stereo cameras
Solution Approach 1:
The patent changes the approach to depth estimation by using self-supervised learning to learn accurate depth parameters from monocular images. Instead of relying on the limited geometric constraints of traditional monocular methods, the system learns rich depth representations through pixel-wise supervision, achieving accuracy comparable to active sensors while maintaining the cost benefits of passive cameras.
Data Source
AI summary
Systems and methods for self-supervised learning for visual odometry using camera images, may include: estimating correspondences between keypoints of a target camera image and keypoints of a context camera image; based on the keypoint correspondences, lifting a set of 2D keypoints to 3D, using a neural camera model; and projecting the 3D keypoints into the context camera image using the neural camera model. Some embodiments may use the neural camera model to achieve the lifting and projecting of keypoints without a known or calibrated camera model.