Trailer Coupler Distance Sensing with Camera-IMU Fusion
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Solution Overview
Problem
Existing systems for determining the distance between a tow vehicle and a trailer are inadequate for accurately calculating longitudinal, lateral, and vertical distances, particularly when hitching the trailer, which can complicate the connection process.
Innovation Solution
A method using a monocular camera and an inertial measurement unit (IMU) to determine distances through an iterated extended Kalman filter, fusing camera image data and IMU sensor data to calculate longitudinal, lateral, and vertical distances to the trailer coupler, with optional autonomous driving assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a monocular camera and IMU are used with sensor fusion to determine distance, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent uses an iterated extended Kalman filter as an intermediary algorithm to fuse sensor data from the monocular camera and IMU. This filter acts as a mediator that combines the optical flow data from the camera with the inertial measurement data, producing accurate distance estimates without requiring complex direct integration of the sensors themselves.
Solution Approach 2:
The patent replaces complex mechanical distance measurement systems (such as multiple cameras or laser rangefinders) with a computational approach using sensor fusion. By substituting mechanical complexity with algorithmic processing of data from simpler sensors, the system achieves high measurement precision while keeping the physical device relatively simple.
2Productivity
If real-time distance measurement is implemented during hitching, then productivity is improved, but device complexity increases
Solution Approach 1:
The system performs real-time distance calculation and provides autonomous driving assistance automatically during the hitching process. The sensor fusion algorithm continuously processes camera and IMU data to update distance estimates without requiring manual intervention, enabling the system to self-manage the hitching guidance function and improve productivity.
Solution Approach 2:
The patent implements continuous real-time distance measurement throughout the entire hitching process. The iterated extended Kalman filter continuously fuses incoming sensor data streams from the camera and IMU, providing uninterrupted distance information that enables continuous guidance and control, thereby maintaining productive action throughout the operation.
3Measurement precision
If multiple sensor data fusion is used to calculate longitudinal, lateral, and vertical distances, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent extracts and processes only the essential features from the camera images (such as trailer coupler position and orientation) and combines them with selected IMU data in the Kalman filter. By extracting only the necessary information elements rather than processing all raw sensor data, the system achieves accurate three-dimensional distance measurement while keeping the computational burden and operational complexity manageable.
Data Source
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AI summary
A method for determining a distance between a camera (410) positioned on a rear portion of a tow vehicle (100) and a trailer coupler (212) supported by a trailer (200) positioned behind the tow vehicle as the tow vehicle approaches the trailer. The method includes identifying the trailer coupler of the trailer within one or more images of a rearward environment of the tow vehicle. The method also includes receiving sensor data from an inertial measurement unit (420) supported by the tow vehicle. The method includes determining a pixel-wise intensity difference between a current received image from the one or more images and a previously received image from the one or more images. The method includes determining the distance based on the identified trailer coupler, the sensor data, and the pixel-wise intensity difference, the distance includes a longitudinal distance (DLg), a lateral distance (DLt), and a vertical distance (HCC).