Map Reconstruction Validation Using Secondary Sensor Constraints
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Solution Overview
Problem
Existing three-dimensional reconstruction methods, particularly those relying on optical imaging, face challenges in ensuring data accuracy and consistency, leading to potential inaccuracies in map creation and updates due to the lack of validation using secondary sensor data.
Innovation Solution
A method involving the use of secondary sensor data such as IMU, GPS, accelerometer, gyroscope, and depth perception sensors to validate and align image data-based reconstructions by applying constraints, ensuring that the sequential pose information aligns with the secondary sensor data, thereby improving the quality and accuracy of map reconstructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If optical imaging techniques are used for three-dimensional reconstruction, then equipment cost is reduced and accessibility is improved, but data accuracy and consistency deteriorate due to lack of validation from secondary sensors
Solution Approach 1:
The patent introduces secondary sensor data (IMU, GPS, accelerometer, gyroscope, depth perception sensors) as an intermediary validation layer. This mediator checks the sequential pose information derived from optical images against independent sensor measurements, identifying and correcting inconsistencies without replacing the cost-effective optical imaging system.
2Reliability
If secondary sensor validation is added to the reconstruction pipeline, then map accuracy and reliability are improved, but system complexity and processing requirements increase
Solution Approach 1:
The patent performs validation checks on sequential pose information before it is permanently integrated into the global map. By preliminarily identifying inconsistencies between optical reconstruction and secondary sensor data, and correcting them in advance, the system ensures high map accuracy while managing complexity through structured preprocessing.
3Measurement precision
If multiple sensor types are integrated for validation, then measurement precision and consistency are improved, but data processing time and computational resources increase
Solution Approach 1:
The patent implements a feedback mechanism where secondary sensor data continuously validates and corrects sequential pose information during the reconstruction process. This feedback loop identifies inconsistencies and triggers targeted corrections, improving measurement precision while optimizing processing time by focusing computational resources on problematic areas rather than reprocessing all data.
Data Source
AI summary
Examples disclosed herein may involve a computing system that is operable to (i) receive image data and corresponding secondary sensor data, (ii) generate a reconstruction of a map from the image data, wherein the reconstruction comprises sequential pose information, (iii) determine constraints from the secondary sensor data, and (iv) validate the reconstruction of the map by applying the determined constraints from the secondary sensor data to the determined sequential pose information from the reconstruction of the map and determining whether the sequential pose information fails to satisfy any of the constraints determined from the secondary sensor data.