Mobile Machine Imaging Adjustment via Monocular Ego-Motion
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Solution Overview
Problem
Current driver assistance and autonomous driving systems require costly redesigns and adjustments due to changes in imaging device arrangements, leading to inefficiencies and unreliability.
Innovation Solution
A method and system that utilize monocular images to estimate the ego-motion of a vehicle, allowing for automatic adjustment of hardware and software configurations by detecting scene features and recalibrating imaging devices based on egomotion estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If imaging devices are replaced or parameters are corrected due to malfunction or movement, then detection reliability is improved, but system complexity and adjustment burden increase
Solution Approach 1:
The system performs self-calibration by automatically detecting scene features in monocular images, estimating egomotion through feature matching, and adjusting imaging device parameters without requiring external calibration equipment or manual intervention. This self-service mechanism maintains detection reliability while minimizing system complexity.
Solution Approach 2:
The system dynamically adjusts imaging device parameters (such as intrinsic parameters and extrinsic parameters) based on detected egomotion from monocular images. By changing parameters adaptively rather than requiring physical device replacement or complex recalibration procedures, the system maintains reliability while reducing adjustment burden.
2Adaptability or versatility
If hardware and software are redesigned to accommodate changes in imaging device arrangements, then adaptability is improved, but development cost and time increase
Solution Approach 1:
The system transitions from static pre-calibrated configurations to dynamic self-adjusting configurations. By continuously estimating egomotion from monocular images and adjusting parameters in real-time, the system adapts to different imaging device arrangements without requiring redesign, thereby improving configurability while reducing development costs.
Solution Approach 2:
The egomotion estimation module serves multiple functions: it compensates for imaging device movements, calibrates multiple imaging devices with different fields of view, and adjusts system parameters adaptively. This multi-functional approach provides universal adaptability across various configurations without requiring separate development for each scenario.
3Area of stationary object
If multiple imaging devices with different fields of view are used to cover surrounding areas, then detection coverage is improved, but calibration difficulty increases
Solution Approach 1:
The system introduces scene features detected from monocular images as an intermediary to establish relationships between multiple imaging devices. By matching these features across different devices and estimating egomotion, the system automatically calibrates devices with different fields of view without requiring complex direct calibration procedures, thereby expanding detection coverage while reducing calibration difficulty.
Data Source
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AI summary
A method for adjusting an information system of a mobile machine (140), the information system configured to calculate 3D information relative to a scene (120) in which the mobile machine is moving, the method including acquiring at least a first image (22) of the scene (120) at a first time (t-1) and a second image (24) of the scene at a second time (t); detecting one or more scene feature (125) in the first image and the second image; matching the one or more scene feature across the first image and the second image based upon detection of the one or more scene feature; estimating an egomotion of the mobile machine based upon the matching of the one or more scene feature across the first image and the second image; and adjusting the information system by taking into account the estimation of the egomotion of the mobile machine.