Camera Pose Estimation Using Multi-Sensor Fusion
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Solution Overview
Problem
In applications like computer vision and SLAM, the estimated camera pose is often unreliable due to factors such as low feature numbers, poor lighting, motion blur, or fast camera motion, leading to resource wastage and delays in generating desired data.
Innovation Solution
A method and device that utilize sensor fusion, including color data, depth data, and inertial measurement unit (IMU) data, to detect camera pose failures and re-localize the camera by selectively choosing reliable sensors for accurate pose correction, using RGB data for initial localization and depth data for finer adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional single-sensor image processing is used, then device complexity is low, but camera pose estimation reliability deteriorates under unconstrained environments
Solution Approach 1:
The patent merges multiple sensor types (color camera, depth sensor, IMU) into a unified pose estimation system. The processor integrates data from all sensors to produce a reliable camera pose, combining the strengths of each sensor to overcome individual limitations in unconstrained environments.
Solution Approach 2:
The system creates a universal pose estimation approach that works across diverse environmental conditions by utilizing multiple sensors. The same multi-sensor framework handles various scenarios (indoor, outdoor, dynamic, static) that would require different single-sensor methods, making the system universally applicable.
2Measurement precision
If multiple sensors are used for pose estimation, then measurement precision improves, but processing power and computational resources increase
Solution Approach 1:
The system applies partial action by selectively processing sensor data based on availability and reliability. When depth data is unavailable, the system uses only color and IMU data. The processing intensity is adjusted dynamically, performing comprehensive multi-sensor fusion only when needed for high precision, and using lighter processing when conditions permit.
Solution Approach 2:
The pose estimation system dynamically adjusts its processing strategy based on real-time conditions. The system monitors sensor quality and environmental factors, switching between different processing modes (full fusion vs. selective fusion) to maintain precision while optimizing computational resource usage.
3Reliability
If continuous pose correction is performed, then reliability of tracking improves, but processing time and system response delay increase
Solution Approach 1:
The system performs preliminary actions by pre-processing sensor data and maintaining updated sensor models before pose estimation is needed. Depth maps, edge detections, and IMU calibration data are prepared in advance, allowing faster pose correction when required without needing to perform all computations from scratch.
Solution Approach 2:
The system maintains continuous useful action by keeping sensor data streams active and pre-processing operations ongoing. The pose estimation system continuously updates its understanding of the environment through background processing, ensuring that when pose correction is needed, the foundation of processed data is already in place to minimize additional processing time.
Data Source
AI summary
A method of determining a pose of a camera is described. The method comprises analyzing changes in an image detected by the camera using a plurality of sensors of the camera; determining if a pose of the camera is incorrect; determining which sensors of the plurality of sensors are providing the most reliable image data; and analyzing data from the sensors providing the most reliable image data.