Multi-Camera Calibration for Stationary and Mobile Systems with Non-Overlapping Views

Core Concepts

CALICO is a pattern-based method for multi-camera calibration that can handle stationary and mobile multi-camera systems, cameras with non-overlapping fields of view, and non-synchronized cameras.

Abstract

The paper presents CALICO, a method for multi-camera calibration that can handle challenging scenarios such as stationary and mobile multi-camera systems, cameras without overlapping fields of view, and non-synchronized cameras. The key highlights are: CALICO formulates the multi-camera calibration problem as a set of rigidity constraints imposed by the transformations between cameras and calibration patterns. The multi-camera calibration problem is solved efficiently by iteratively solving for variables using closed-form methods, minimizing algebraic error over the set of rigidity constraints, and minimization of reprojection errors. CALICO is evaluated on both simulated and real-world datasets, including box-type, robot-type, stereo, and wide-baseline stereo configurations. The results show that CALICO can achieve sub-pixel reprojection error and less than 1.11 mm mean reconstruction accuracy error. Compared to the Kalibr toolbox, CALICO performs well in situations where Kalibr fails, such as when there is no overlap between camera views or when the camera rig motion is purely rotational.

Stats

"Mean reconstruction accuracy error was ≤0.71 mm for real camera rigs, and ≤1.11 for simulated camera rigs." "CALICO compared favorably to Kalibr."

Quotes

"CALICO is a pattern-based approach, where the multi-calibration problem is formulated using rigidity constraints between patterns and cameras." "We use a pattern rig: several patterns rigidly attached to each other or some structure."

Key Insights Distilled From

Multi-camera calibration with pattern rigs, including for non-overlapping cameras

by Amy Tabb,Hen... at arxiv.org 03-29-2024

https://arxiv.org/pdf/1903.06811.pdf

Deeper Inquiries

How could CALICO be extended to handle dynamic scenes or moving objects within the camera views

To extend CALICO to handle dynamic scenes or moving objects within the camera views, we would need to incorporate techniques for motion estimation and tracking. One approach could be to implement a feature tracking algorithm that can follow specific points or patterns in the scene as they move. This would involve updating the calibration parameters in real-time based on the tracked features. Additionally, integrating a mechanism for handling occlusions and changes in the scene geometry would be crucial to ensure accurate calibration in dynamic environments. By continuously updating the calibration parameters based on the movement of objects in the scene, CALICO could adapt to changing conditions and maintain accurate multi-camera calibration.

What are the limitations of CALICO compared to infrastructure-based multi-camera calibration methods, and how could these be addressed

One limitation of CALICO compared to infrastructure-based multi-camera calibration methods is its reliance on pattern-based approaches, which may not be suitable for all scenarios. Infrastructure-based methods, such as Structure-from-Motion frameworks, can leverage the scene's features to calibrate cameras, providing more flexibility in various settings. To address this limitation, CALICO could be enhanced by incorporating a hybrid approach that combines pattern-based and infrastructure-based techniques. By integrating the ability to utilize scene features for calibration in addition to calibration patterns, CALICO could improve its adaptability to different environments and camera configurations.

How could the CALICO approach be applied to calibrate multi-sensor systems beyond just cameras, such as cameras and LiDAR units

To apply the CALICO approach to calibrate multi-sensor systems beyond just cameras, such as cameras and LiDAR units, the method would need to be extended to handle the unique characteristics and data types of each sensor. For LiDAR units, which provide depth information, the calibration process would involve establishing correspondences between the LiDAR data and the camera images. This could be achieved by incorporating LiDAR point clouds into the calibration constraints and optimizing the sensor poses accordingly. Additionally, the calibration patterns used in CALICO could be designed to be detectable by both cameras and LiDAR sensors, enabling the calibration of the entire multi-sensor system. By integrating the calibration of multiple sensor modalities, CALICO could provide a comprehensive solution for calibrating complex multi-sensor setups.