Camera Laser Scanner Calibration Scene Feature Matching
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Solution Overview
Problem
Current methods for calibrating cameras and laser scanners for sensor fusion are inefficient, often requiring precise calibration objects that can be costly, difficult to obtain, or prone to errors, and involve manual processes that are imprecise and labor-intensive.
Innovation Solution
A method that uses a comparison of camera images and remission images to determine the offset and orientation between the camera and laser scanner, allowing for automatic extrinsic calibration without the need for calibration objects, by reconstructing distances in the camera image using laser scanner measurements and projecting features onto a unit sphere for correspondence search in three-dimensional space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration methods are used to determine the transformation between camera and laser scanner, then the process is simple to understand, but the calibration precision is low and the process is labor-intensive
Solution Approach 1:
The system performs automatic extrinsic calibration by having the laser scanner scan the scene and automatically comparing the remission image with the camera image. The transformation parameters are determined through automated correspondence search and optimization algorithms, eliminating the need for manual calibration operations while achieving high precision through iterative refinement of the extrinsic matrix.
2Measurement precision
If precise calibration objects are used to improve calibration accuracy, then the measurement precision improves, but the cost and difficulty of obtaining these objects increases
Solution Approach 1:
The invention extracts the calibration process from dependence on special calibration objects by using the natural scene itself as the calibration target. The laser scanner scans the actual scene to be measured, and the remission image from this scan is directly compared with the camera image, eliminating the need to introduce separate calibration objects into the measurement environment.
Solution Approach 2:
The laser scanner serves dual purposes: it both measures the scene geometry for the final application and simultaneously provides the data needed for extrinsic calibration. The same scanning operation that captures the remission image for comparison also establishes the transformation parameters, making the calibration process universally applicable to any scene without requiring scene-specific calibration objects.
3Reliability
If calibration objects are used to establish correspondence between sensors, then the initial calibration can be achieved, but the calibration objects can change over time due to wear or color loss, affecting reliability
Solution Approach 1:
The system uses the actual measurement scene as the calibration reference, which inherently remains stable and does not degrade over time. By performing the calibration using the scene's own features visible to both sensors, the method eliminates dependence on physical calibration objects that could wear, lose color, or become damaged, thereby ensuring long-term calibration stability without requiring maintenance or replacement of calibration artifacts.
4Measurement precision
If traditional extrinsic calibration methods are used, then the transformation can be determined, but the process requires defined calibration objects that are costly or difficult to obtain
Solution Approach 1:
The invention removes the requirement for special calibration objects by extracting the calibration information directly from the natural scene. The laser scanner scans the scene as it would be scanned during normal operation, and the remission image is compared with the camera image to determine the extrinsic transformation, thereby extracting calibration data from the operational environment itself rather than requiring a separate calibration setup.
Solution Approach 2:
Instead of using a calibration object to mediate between the two sensors, the method inverts the approach by using the scene features themselves as the correspondence basis. The correspondence search is performed on features detected in both the camera image and the remission image, reversing the traditional flow where a calibration object would be the common reference.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables reproducible, high-quality calibration results with reduced effort and cost, delivering a stable transformation estimate without the need for manual intervention or precise calibration objects, and can be evaluated using quality criteria.
Implementation Method 1
Such distance-measuring laser scanners work in accordance with a time of flight principle in which the time of flight from the scanner into the scene and back is measured and distance data are calculated using the speed of light
Implementation Method 2
In phase-based methods, the light transmitter modulates the scanning beam and the phase between a reference and the received scanning beam is determined
Implementation Method 3
the camera can detect the visual impression of the scene and can thus detect properties such as lighting, materials, or textures
Data Source
AI summary
A method of calibrating a camera and a laser scanner for their sensor fusion is provided whereby the camera records a camera image of a scene and the laser scanner at least partly scans the scene with a scanning beam and records a remission image from the respective angular position of the scanning beam and from the intensity determined at the angular position of the scanning beam remitted from the scene, wherein an offset and/or a relative orientation between the camera and the laser scanner is determined from a comparison of the camera image and the remission image. Distances with respect to the remission image are here also determined by the laser scanner and distances with respect to the camera image are reconstructed; and in that a correspondence search of corresponding features in the camera image and in the remission image is carried out in three-dimensional space for the comparison.