eKalibr: Dynamic Intrinsic Calibration for Event Cameras From First Principles of Events

TL;DR

eKalibr introduces a novel, accurate, and convenient intrinsic calibration method for event cameras using event-based pattern recognition and ellipse fitting, eliminating complex instrumentation and relying on first principles.

Contribution

It presents the first event-based calibration approach that leverages pattern recognition and ellipse fitting, improving accuracy and simplicity over existing methods.

Findings

High accuracy in pattern extraction and calibration

Effective handling of dynamic scenes with high contrast

Open-source implementation available for research use

Abstract

The bio-inspired event camera has garnered extensive research attention in recent years, owing to its significant potential derived from its high dynamic range and low latency characteristics. Similar to the standard camera, the event camera requires precise intrinsic calibration to facilitate further high-level visual applications, such as pose estimation and mapping. While several calibration methods for event cameras have been proposed, most of them are either (i) engineering-driven, heavily relying on conventional image-based calibration pipelines, or (ii) inconvenient, requiring complex instrumentation. To this end, we propose an accurate and convenient intrinsic calibration method for event cameras, named eKalibr, which builds upon a carefully designed event-based circle grid pattern recognition algorithm. To extract target patterns from events, we perform event-based normal flow estimation to identify potential events generated by circle edges, and cluster them spatially. Subsequently, event clusters associated with the same grid circles are matched and grouped using normal flows, for subsequent time-varying ellipse estimation. Fitted ellipse centers are time-synchronized, for final grid pattern recognition. We conducted extensive experiments to evaluate the performance of eKalibr in terms of pattern extraction and intrinsic calibration. The implementation of eKalibr is open-sourced at (https://github.com/Unsigned-Long/eKalibr) to benefit the research community.