Camera-IMU Bundle Adjustment for AR Sensor Drift Calibration
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Solution Overview
Problem
Existing augmented reality (AR) devices face inaccuracies in environment mapping and localization due to changes in the relative positions of multiple cameras, which are not accurately accounted for by current bundle adjustment processes, leading to less precise 3D map generation and device positioning.
Innovation Solution
A method that integrates inertial data from IMUs with camera data for joint optimization of camera and IMU calibrations during bundle adjustment, using a combination of visual and inertial measurements to correct relative positions and enhance calibration accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If factory-calibrated sensor parameters are used without recalibration, then device complexity is reduced, but measurement precision deteriorates due to changes in relative positions of cameras and sensors
Solution Approach 1:
The system performs preliminary bundle adjustment calculations using factory-calibrated parameters to establish initial 3D maps and device positions. This preliminary action allows the system to operate with reduced complexity initially, then progressively refine accuracy through subsequent calibration updates without requiring full recalibration from scratch.
Solution Approach 2:
The system implements feedback mechanisms where bundle adjustment results are used to detect drift in relative positions between cameras and sensors. When drift exceeds thresholds, the system triggers recalibration sequences, creating a closed-loop system that maintains measurement precision while minimizing unnecessary calibration operations that会增加 device complexity.
2Measurement precision
If bundle adjustment is performed frequently to maintain accuracy, then measurement precision is improved, but loss of time increases due to computational overhead
Solution Approach 1:
Instead of performing complete bundle adjustment frequently, the system applies partial adjustments only to affected portions of the 3D map when drift is detected. This selective approach maintains measurement precision in critical areas while reducing overall computational time by avoiding redundant calculations in stable regions.
Solution Approach 2:
The system implements periodic bundle adjustment at scheduled intervals combined with event-triggered adjustments when drift thresholds are exceeded. This hybrid timing strategy balances maintaining 3D map accuracy with minimizing computational overhead by performing intensive calculations only when necessary rather than continuously.
3Measurement precision
If relative positions of cameras are corrected using inertial data, then measurement precision is improved, but device complexity increases due to integration of multiple sensor types
Solution Approach 1:
The system merges data from inertial measurement units (IMUs) with camera observations within a unified bundle adjustment framework. By combining these sensor types into a single integrated calibration process, the system improves camera position accuracy through complementary sensor data while managing complexity through unified processing rather than separate correction systems.
Solution Approach 2:
The bundle adjustment system is designed to handle multiple sensor types (cameras, IMUs, and potentially other sensors) through a universal mathematical framework. This multi-functional approach allows the same core algorithm to process different sensor data types, improving measurement precision across all sensors while avoiding the need for separate specialized processing systems that would increase overall device complexity.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for calibrating an augmented reality device using camera and inertial measurement unit data. In some implementations, a bundle adjustment process jointly optimizes or estimates states of the augmented reality device. The process can use, as input, visual and inertial measurements as well as factory-calibrated sensor extrinsic parameters. The process performs bundle adjustment and uses non-linear optimization of estimated states constrained by the measurements and the factory calibrated extrinsic parameters. The process can jointly optimize inertial constraints, IMU calibration, and camera calibrations. Output of the process can include most likely estimated states, such as data for a 3D map of an environment, a trajectory of the device, and/or updated extrinsic parameters of the visual and inertial sensors (e.g., cameras and IMUs).