Colored Point Cloud Pose Refinement Without Lidar Synchronization
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Solution Overview
Problem
Existing methods for training machine learning models to estimate the pose of a receiver aircraft for air-to-air refueling operations face challenges due to the difficulty in obtaining accurate pose estimates, as GPS data lacks fidelity, and data from sensors like external lidars or IMUs are difficult to synchronize with camera frame timing and geometric precision.
Innovation Solution
A computer-implemented method and system that uses a pose estimator to refine pose estimates by adjusting initial estimates using frames and colored point clouds, iteratively determining updated point cloud colors until a threshold is met, enabling more accurate pose estimation for training machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If GPS data is used for pose estimation, then the system is simple to implement, but the measurement precision is insufficient
Solution Approach 1:
The patent combines multiple data sources (camera frames, colored point clouds, initial pose estimates) into a unified pose estimation system. The refined pose estimator integrates visual data from cameras with point cloud color information to produce high-precision pose estimates, merging otherwise incompatible data types into a coherent estimation framework that achieves both accuracy and practical implementability.
Solution Approach 2:
The patent introduces colored point clouds as an intermediary representation between raw camera images and final pose estimates. The point cloud color estimation process acts as a mediator that bridges visual observations with geometric pose parameters, enabling the system to achieve high measurement precision while maintaining reasonable system complexity through this intermediate computational layer.
2Measurement precision
If external lidars or IMUs are used for pose estimation, then the measurement precision can be improved, but the device complexity and synchronization difficulty increase
Solution Approach 1:
The patent extracts pose estimation capability directly from visual data (camera frames and point cloud colors) without requiring external lidar or IMU sensors. By taking out the dependency on these complex external sensors and deriving pose information purely from computer vision data, the system achieves sufficient measurement precision while eliminating the device complexity and synchronization issues associated with multi-sensor fusion.
3Measurement precision
If iterative refinement is performed to improve pose accuracy, then the measurement precision increases, but the computation time increases
Solution Approach 1:
The patent performs preliminary color estimation for the point cloud before the iterative refinement process. By pre-computing the colored point cloud representation from camera frames, the system prepares essential data structures in advance, allowing the subsequent iterative pose refinement to converge faster with fewer iterations, thus reducing overall computation time while maintaining high measurement precision.
Data Source
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AI summary
A pose estimation system comprising a computer system and a pose estimator located in the computer system. The pose estimator is configured to estimate initial point cloud colors (222) for points (223) in a colored point cloud (224, 410) of a surface (225) of an object (201) using frames (221) in a video (105, 202) of the object (201) and initial pose estimate (241) for the object (201) in the frames (221). The pose estimator is configured to adjust the initial pose estimate (241) using the frames (221) and the colored point cloud (224, 410) to form updated pose estimates (227). The pose estimator is configured to determine updated point cloud colors (226) for the points (223) in the colored point cloud (224, 410) using the frames (221) in the video (105, 202) and the updated pose estimates (227). The pose estimator is configured to repeat adjusting the updated pose estimates (227) using the frames (221) and the colored point cloud (224, 410) and determining the updated point cloud colors (226) for the points (223) in the colored point cloud (224, 410) using the frames (221) in the video (105, 202) and the updated pose estimates (227) until the updated pose estimates (227) meet a threshold (228).