IMU Camera Integration with Navigation Feedback for Feature Tracking
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Solution Overview
Problem
Navigation devices with low-cost inertial sensors are prone to rapid error accumulation, necessitating the integration of secondary sensors like cameras or LIDAR to reduce errors, but this leads to high processing demands and uncorrected errors due to independent operation of IMUs and secondary sensors.
Innovation Solution
A navigation device integrating an inertial measurement unit (IMU) with a monocular camera and a processor that extracts features from image frames, using inertial measurements and feature tracking to estimate navigation data, with a hybrid extended Kalman filter to integrate sensor data and correct errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If low-cost inertial sensors are used, then device cost is reduced, but navigation accuracy deteriorates due to rapid error accumulation
Solution Approach 1:
The patent combines IMU and camera into a single integrated navigation device with a unified processor that fuses inertial measurements and image data. This merging allows the system to leverage both low-cost IMU and camera sensors together, achieving accurate navigation through data fusion rather than relying on expensive high-precision IMU alone.
Solution Approach 2:
The processor acts as an intermediary that fuses data from the IMU and camera sensors. It integrates inertial measurements with image-based navigation data, reconciling the rapid error accumulation of the IMU with the visual constraints from the camera to produce accurate navigation solutions.
2Device complexity
If IMU and camera operate independently, then device complexity is reduced, but navigation accuracy deteriorates due to uncorrected errors
Solution Approach 1:
The patent merges the operational workflows of the IMU and camera through a unified processing architecture. The processor simultaneously handles inertial measurements and image data, integrating them into a cohesive navigation solution that corrects errors through mutual constraint rather than independent operation.
Solution Approach 2:
The system implements feedback loops where the processor continuously refines the navigation solution by comparing IMU-derived predictions with camera-based observations. This feedback mechanism allows real-time error correction, where discrepancies between inertial and visual data are used to adjust and improve navigation accuracy.
3Productivity
If feature extraction is performed without navigation data feedback, then processing speed is increased, but measurement precision deteriorates due to uncorrected feature tracking errors
Solution Approach 1:
The processor uses navigation data derived from inertial measurements to provide feedback to the feature extraction and tracking process. This feedback constrains the search space for feature matching and corrects tracking drift, maintaining both processing efficiency and feature tracking accuracy through iterative refinement.
Data Source
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AI summary
A navigation device is provided herein comprising an inertial measurement unit (IMU), a camera, and a processor. The IMU provides an inertial measurement to the processor and the camera provides at least one image frame to the processor. The processor is configured to determine navigation data based on the inertial measurement and the at least one image frame, wherein at least one feature is extracted from the at least one image frame based on the navigation data.