Multi-Camera IMU Calibration Using 3D Pose Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional online calibration methods for cameras and IMUs in visual and inertial navigation systems result in low accuracy parameters due to the difficulty in accurately reflecting the relative position and attitude transformation relationship between the camera and IMU, which are mounted at different positions on a carrier platform.
Innovation Solution
A method involving the use of a terminal to obtain image sequences from multiple cameras, determine matching feature points, filter these points based on a relationship matrix, convert them into three-dimensionally distributed points, and optimize calibration parameters between the IMU and cameras by aligning poses, using techniques such as superglue feature point matching, deep learning algorithms, and preintegration to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If online calibration is performed on a single camera and IMU using conventional methods, then the calibration process is simple, but the calibration accuracy is low
Solution Approach 1:
The patent segments the calibration process into multiple independent calibration units, each corresponding to a different camera viewing direction (front, rear, left, right). Each calibration unit processes image sequences from its specific camera independently, then integrates results. This segmentation allows comprehensive multi-directional calibration while maintaining manageable complexity through modular processing.
Solution Approach 2:
The patent transitions from single-camera 2D image calibration to multi-camera 3D spatial calibration by introducing the dimension of viewing direction. Multiple cameras capture images from different spatial angles, and the system integrates these multi-dimensional observations to achieve accurate 3D pose estimation and calibration parameters.
2Measurement precision
If multiple cameras are used for calibration, then the calibration accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent divides the computational workload into separate calibration units, each handling one camera's image sequences independently. This segmentation parallelizes computation across multiple processing threads or devices, reducing the computational burden on any single processing unit while maintaining the benefits of multi-camera calibration.
Solution Approach 2:
The patent performs preliminary feature point extraction and matching within each calibration unit before integration. By pre-processing image sequences to extract key feature points and establish initial correspondences, the system reduces the computational complexity of the subsequent pose estimation and calibration parameter optimization stages.
3Measurement precision
If feature point matching is performed without filtering, then the processing speed is fast, but the calibration precision is low due to noise
Solution Approach 1:
The patent performs preliminary filtering of feature points based on geometric constraints (epipolar geometry, fundamental matrix) before using them for calibration. This pre-filtering removes obvious outliers and noise while preserving valid feature correspondences, improving calibration precision without requiring extensive iterative refinement later.
Solution Approach 2:
The patent implements efficient filtering that quickly eliminates invalid feature points using geometric consistency checks, allowing the system to skip detailed verification of obviously incorrect matches. This rapid filtering approach maintains high precision by removing noise while minimizing the time spent on feature point processing.
Data Source
Figure 1
Figure 2
Figure 3a
AI summary
This application relates to a method and apparatus for calibrating cameras and an inertial measurement unit (IMU), a computer device, a storage medium, and a computer program product. The method may be applied to application scenarios such as online calibration of autonomous driving, electronic maps, and sensors. The method includes: obtaining image sequences acquired by cameras (S202); matching feature points of different images in the image sequences, to obtain matching feature points (S204); filtering the matching feature points to obtain target feature points (S206); converting the target feature points into three-dimensionally distributed points, and determining poses of the cameras based on the three-dimensionally distributed points and pixels of the images in the image sequences that correspond to the three-dimensionally distributed points (S208); and optimizing calibration parameter between an IMU and the cameras according to the poses of the cameras and a pose of the IMU (S210).