ROV Position Estimation Using 2D Registration in Noisy Telemetry
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Solution Overview
Problem
ROVs operating in underwater environments face challenges with noisy positional telemetry, leading to misalignment between virtual and real elements, which hinders accurate navigation and operation.
Innovation Solution
A system and method that includes a position estimation module to remove outlier estimations, a full registration process for specific structures, and an efficiency module to enhance the alignment of virtual and real elements, using a combination of modules such as 2D projection, registration, and efficiency modules to improve position estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ROV positional telemetry is used directly for AR rendering, then the system is simple and fast, but the alignment between virtual and real elements becomes inaccurate due to noisy position data
Solution Approach 1:
The patent introduces an intermediary position estimation module that processes the noisy ROV positional telemetry before using it for AR rendering. This module acts as a mediator between the raw sensor data and the rendering system, applying filtering algorithms (such as Kalman filtering or complementary filtering) to reduce noise and improve position accuracy without requiring hardware changes
Solution Approach 2:
The system implements feedback by continuously comparing the estimated ROV position with visual feedback from the camera feed and 3D model alignment. The position estimation module uses this feedback to continuously refine and correct the ROV position estimates, creating a closed-loop system that improves accuracy over time
2Measurement precision
If full registration process is performed for all structures, then alignment accuracy is maximized, but computational efficiency and real-time performance deteriorate
Solution Approach 1:
The patent applies partial registration by performing full registration processes only for selected key structures or when significant alignment errors are detected, rather than continuously registering all structures. This selective approach maintains alignment accuracy for critical elements while reducing overall computational burden and improving real-time performance
Solution Approach 2:
The registration process is segmented into different levels: a fast approximate registration for continuous operation and a more accurate full registration performed selectively. This segmentation allows the system to maintain real-time performance while periodically improving alignment accuracy when needed
Data Source
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AI summary
The present invention provides a system and method of position estimation for remotely operated vehicles, even in noisy environments. In some embodiments, a position estimation engine includes a 2D projection module, a registration module, a position estimation module, and an efficiency module. The improved position estimation starts with a real frame from a video and a virtual image that is the projection of the 3D elements given the ROV's noisy position. The position estimate begins by projecting each of the visible structures individually and then registers them with the real image. Then the 2D transformation resulting from the registration process is used to estimate the 3D ROV's position. Then, the ROV's position estimates are robustly combined. Because this position estimation needs to run in real-time or near real-time, an efficiency module improves the efficiency of the position estimation.