Dynamic Event Camera Calibration

Camera calibration is an important prerequisite towards the solution of 3D computer vision problems. Traditional methods rely on static images of a calibration pattern. This raises interesting challenges towards the practical usage of event cameras, which notably require image change to produce sufficient measurements. The current standard for event camera calibration therefore consists of using flashing patterns. They have the advantage of simultaneously triggering events in all reprojected pattern feature locations, but it is difficult to construct or use such patterns in the field. We present the first dynamic event camera calibration algorithm. It calibrates directly from events captured during relative motion between camera and calibration pattern. The method is propelled by a novel feature extraction mechanism for calibration patterns, and leverages existing calibration tools before optimizing all parameters through a multi-segment continuous-time formulation. As demonstrated through our results on real data, the obtained calibration method is highly convenient and reliably calibrates from data sequences spanning less than 10 seconds.

Authors

• Kun Huang
• Yifu Wang
• Laurent Kneip

Keywords

• Three-dimensional displays
• Heuristic algorithms
• Dynamics
• Robot vision systems
• Propulsion
• Cameras
• Calibration
• Camera Calibration
• Dynamic Vision Sensor
• Dynamic Calibration
• Calibration Method
• Reference Frame
• Cubic Spline
• Control Points
• Time Stamp
• Illumination Conditions
• Detection Of Patterns
• Intrinsic Parameters
• Lie Group
• Motion Compensation
• Extrinsic Parameters
• Calibration Target
• Trajectory Segments
• World Frame
• Camera Intrinsic Parameters
• Event Stream
• Types Of Lenses
• Regular Calibration
• Perspective Camera
• Unit Quaternion
• Ground Truth Trajectory
• Negative Cluster
• Cluster Centers
• Camera Pose
• Feature Extraction Methods
• Image Distortion
• Normal Images