Load-Carrier Pose Estimation Through 2D–3D Keypoint Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing Load-carrier Pose estimation systems are limited by sensor type-specific processing steps, struggle with oblique observation angles, and fail to uniquely detect a single Load-carrier among multiple, especially when there is relative motion between the vehicle and the Load-carrier.
Innovation Solution
A system combining a 2D sensor and a 3D sensor for Load-carrier detection and Pose estimation, utilizing 2D Keypoint detection, 3D matching, and 3D template matching to accurately determine the Pose of Load-carriers from various angles and distances, even with relative motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If 2D sensor data is used for Load-carrier detection, then detection speed is improved, but measurement precision of 3D position and orientation deteriorates
Solution Approach 1:
The system merges 2D sensor data (from camera) and 3D sensor data (from depth sensor) to perform Load-carrier detection and Pose estimation. The 2D sensor provides fast detection capability while the 3D sensor supplies depth information for accurate 3D position and orientation measurement. This combination resolves the contradiction by integrating the strengths of both sensor types.
Solution Approach 2:
The system transitions from 2D image data to 3D spatial understanding by incorporating depth information from a 3D sensor. The 2D Keypoints detected in the image are matched with 3D data points, enabling the system to estimate 3D Pose (position and orientation) while maintaining the speed advantage of 2D detection.
2Adaptability or versatility
If traditional Pose estimation methods are used, then processing simplicity is maintained, but adaptability to oblique observation angles deteriorates
Solution Approach 1:
The system uses 3D data points to represent Load-carrier features in three-dimensional space, allowing accurate detection from oblique angles. By matching 2D Keypoints with their corresponding 3D data points and computing 3D Pose, the system adapts to various observation angles while maintaining reasonable processing complexity through efficient coordinate transformations.
3Ease of operation
If rigid positioning systems are used, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system computes the Pose of the Load-carrier relative to the vehicle in real-time, allowing the vehicle to maneuver freely while maintaining accurate position and orientation estimation. The dynamic Pose calculation adapts to changing relative positions and orientations, providing both operational flexibility and measurement precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and flexible Load-carrier Pose estimation from wider ranges and oblique angles, allowing vehicles to maneuver more freely and reducing the need for rigid positioning systems.
Implementation Method 1
a 2D sensor... detect 2D Key-points in images of the Load-carrier obtained with the 2D sensor
Implementation Method 2
a 3D sensor... match these 2D Keypoints with 3D data captured with the 3D sensor to establish a 3D position and orientation
Data Source
Figure 1~2
Figure 3~4
Figure 5
AI summary
A system for Load-carrier Pose estimation, comprising a Load-carrier for supporting a load, wherein the Load-carrier is provided with Load-carrier handling indicators or Key-points, and a driving vehicle, wherein the said Pose represents a relative position and orientation of the Load-carrier with reference to the vehicle, said system further comprising a 2D sensor and a 3D sensor, wherein the system is arranged to detect 2D Keypoints in images of the Load-carrier obtained with the 2D sensor, and to match these 2D Keypoints with 3D data captured with the 3D sensor to establish a 3D position and orientation of the detected Keypoints.