Map Reconstruction Fusion Using Secondary Sensor Validation
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Solution Overview
Problem
Current methods for creating and updating large-scale global maps face challenges in ensuring data accuracy and alignment, particularly when using optical imaging techniques that rely on image data from standard camera devices, as they often lack validation and alignment with secondary sensor data, leading to potential inaccuracies and inconsistencies in three-dimensional reconstructions.
Innovation Solution
The method involves receiving image data and corresponding secondary sensor data, generating a map reconstruction with sequential pose information, determining constraints from the secondary sensor data, and validating the reconstruction by applying these constraints to the pose information, ensuring alignment and updating the map if the constraints are satisfied. This process incorporates data from sensors like IMU, GPS, accelerometers, gyroscopes, and depth perception sensors to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If optical imaging techniques using standard camera devices are used for three-dimensional reconstruction, then equipment cost is reduced and accessibility is improved, but measurement precision and data reliability deteriorate due to lack of validation mechanisms
Solution Approach 1:
The patent combines optical imaging data from standard camera devices with secondary sensor data (IMU, GPS, accelerometer, gyroscope, depth perception sensors) to create a multi-sensor validation system. This merging allows the system to maintain accessibility while improving measurement precision through cross-validation of pose information from multiple independent sources.
Solution Approach 2:
The system implements a feedback mechanism where secondary sensor data provides validation constraints for the reconstruction process. The pose information derived from optical imaging is continuously checked against constraints from secondary sensors, and inconsistencies trigger re-evaluation or correction, ensuring accuracy while maintaining the use of accessible camera equipment.
2Reliability
If map reconstructions are updated frequently to improve currency and relevance, then data usefulness is improved, but data consistency and alignment quality deteriorate due to accumulation of errors
Solution Approach 1:
The patent applies validation constraints from secondary sensor data before finalizing map updates. By performing preliminary checks on pose information alignment and consistency against multiple sensor sources before incorporating new reconstruction data, the system ensures that frequent updates maintain both currency and consistency, preventing error accumulation.
3Measurement precision
If validation processes are applied to verify pose information against secondary sensor data, then measurement precision is improved, but computational complexity and processing time increase
Solution Approach 1:
The system implements selective validation where secondary sensor constraints are applied to verify pose information only when necessary or when confidence thresholds are not met. This partial action approach maintains measurement precision through targeted validation while avoiding the computational overhead of exhaustive validation in all scenarios.
Data Source
AI summary
Examples disclosed herein may involve a computing system that is operable to (i) receive one or more images related to a global map having a plurality of overlapping map segments, wherein each of the plurality of overlapping map segments overlaps with one or more neighboring map segments, (ii) based on a preliminary location determination for the one or more images, identify at least a first overlapping map segment of the plurality of overlapping map segments that corresponds to the one or more images, (iii) generate a reconstruction of the first identified overlapping map segment based on the one or more images, and (iv) fuse the generated reconstruction of the first identified overlapping map segment together with the first identified overlapping map segment's one or more neighboring map segments based on overlapping map portions between the generated reconstruction and the first identified overlapping map segment's one or more neighboring map segments.