Colored Point Cloud Pose Refinement for Accurate 6DoF Estimation
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Solution Overview
Problem
Obtaining accurate 6DoF pose estimates for a receiver aircraft in air-to-air refueling operations is challenging due to the difficulty in obtaining precise ground truth data, as GPS data lacks fidelity, and data from sensors like external lidars or IMUs are difficult to synchronize with camera frame timing and geometry.
Innovation Solution
A computer-implemented method and system that estimates initial point cloud colors and pose estimates using frames in a video, iteratively adjusting these estimates until they meet a threshold, refining the pose estimation process to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
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 including video frames, initial pose estimates, and colored point cloud data to create a comprehensive pose estimation system. This merging of multiple information sources allows the system to achieve high measurement precision while maintaining practical implementability through integrated processing.
Solution Approach 2:
The patent introduces an iterative refinement process that acts as an intermediary between rough initial pose estimates and final accurate pose estimates. This intermediary refinement mechanism uses colored point cloud data and video frames to progressively improve accuracy, bridging the gap between simple implementation and high precision.
2Measurement precision
If external lidars or IMUs are used for pose estimation, then the measurement precision can be improved, but the device complexity increases
Solution Approach 1:
The patent creates a virtual colored point cloud model that copies and represents the physical environment and object geometry. This digital copy allows the system to achieve high measurement precision through computational processing of video data, eliminating the need for complex physical sensors like external lidars or IMUs.
Solution Approach 2:
The patent replaces mechanical sensing systems (external lidars, IMUs) with a computational approach using video frames and iterative optimization. This substitution uses software-based processing to achieve the same measurement precision goals without the hardware complexity and synchronization issues associated with mechanical sensors.
3Measurement precision
If iterative refinement is performed multiple times, then the measurement precision improves, but the loss of time increases
Solution Approach 1:
The patent performs preliminary actions by generating initial pose estimates and colored point cloud data before the iterative refinement process. This preliminary preparation ensures that the iterative refinement starts from informed initial conditions, reducing the number of iterations needed and thereby minimizing time loss while maintaining measurement precision.
Solution Approach 2:
The patent implements a feedback mechanism where each iterative refinement cycle uses the results from the previous cycle to improve the pose estimate. This feedback loop efficiently converges to high precision solutions by continuously reducing errors, optimizing the balance between measurement precision and processing time through intelligent use of computational resources.
Data Source
AI summary
A pose estimation system comprising a computer system and a pose estimator. The pose estimator is configured to estimate initial point cloud colors for points in a colored point cloud of a surface of an object using frames in a video of the object and initial pose estimates for the object in the frames; adjust the initial pose estimates using the frames and the colored point cloud to form updated pose estimates; and determine updated point cloud colors for the points in the colored point cloud using the frames in the video and the updated pose estimates. The pose estimator is configured to repeat adjusting the updated pose estimates using the frames and the colored point cloud and determining the updated point cloud colors for the points using the frames in the video and the updated pose estimates until the updated pose estimates meet a threshold.