ATOM Calibration Framework: Interaction and Visualization Functionalities

Area of Science:

• Robotics
• Computer Vision
• Sensor Fusion

Background:

• Modern robotic systems utilize numerous sensors with diverse modalities.
• Accurate estimation of geometric transformations between sensors (sensor calibration) is crucial for system operation.
• Existing calibration approaches involve specific formulations and frameworks, impacting usability.

Purpose of the Study:

• To introduce a novel sensor calibration framework offering enhanced user flexibility, control, and information.
• To improve the user interface and user experience in calibrating complex robotic systems.
• To present a framework supported by the Atomic Transformations Optimization Method (ATOM) formulation.

Main Methods:

• Development of a flexible calibration framework with visualization and interaction functionalities.
• Integration of sensor initial pose estimation, data collection/labeling, and data review/correction.
• Utilizing the Atomic Transformations Optimization Method (ATOM) formulation for transformation estimation.
• Application to various robotic systems with different configurations and sensor types.

Main Results:

• The proposed framework demonstrates applicability across diverse robotic systems, sensor configurations, and modalities.
• The framework provides enhanced user control, visualization, and interaction during calibration.
• A comparative survey indicates a superior user experience with the ATOM framework compared to existing methods.

Conclusions:

• The novel ATOM calibration framework significantly improves the user experience and flexibility for robotic sensor calibration.
• ATOM's design is versatile, supporting a wide range of robotic systems and sensor modalities.
• The framework's interactive features and visualization tools aid in accurate parameter estimation and data management.