EF-Calib: Spatiotemporal Calibration of Event- and Frame-Based Cameras Using Continuous-Time Trajectories

TL;DR

EF-Calib provides a convenient and accurate toolbox for calibrating the system that fuses events and frames and outperforms current methods by achieving the most accurate intrinsic parameters, comparable accuracy in extrinsic parameters to frame-based method, and precise time offset estimation.

Abstract

Event camera, a bio-inspired asynchronous triggered camera, offers promising prospects for fusion with frame-based cameras owing to its low latency and high dynamic range. However, calibrating stereo vision systems that incorporate both event and frame-based cameras remains a significant challenge. In this letter, we present EF-Calib, a spatiotemporal calibration framework for event- and frame-based cameras using continuous-time trajectories. A novel calibration pattern applicable to both camera types and the corresponding event recognition algorithm is proposed. Leveraging the asynchronous nature of events, a derivable piece-wise B-spline to represent camera pose continuously is introduced, enabling calibration for intrinsic parameters, extrinsic parameters, and time offset, with analytical Jacobians provided. Various experiments are carried out to evaluate the calibration performance of EF-Calib, including calibration experiments for intrinsic parameters, extrinsic parameters, and time offset. Experimental results show that EF-Calib achieves the most accurate intrinsic parameters compared to current SOTA, the close accuracy of the extrinsic parameters compared to the frame-based results, and accurate time offset estimation. EF-Calib provides a convenient and accurate toolbox for calibrating the system that fuses events and frames. The code of this paper will also be open-sourced at: https://github.com/wsakobe/EF-Calib.

Figures (8)

• Figure 1: Overview diagram of EF-Calib. (a) The novel calibration pattern consists of the concentric circle and crosspoint. (b) The stereo vision system consists of an event camera and a frame-based camera. (c) The calibration process of EF-Calib.
• Figure 2: Flowchart of the proposed calibration framework
• Figure 3: The pipeline of event-based feature recognizer.
• Figure 4: Schematic of the moving ellipse model. The set of events belonging to the same elliptical feature can be considered as a three-dimensional oblique elliptical cylinder.
• Figure 5: Schematic of a piece-wise B-spline trajectory. The number of event features inside the red box is insufficient; therefore, this segment of the trajectory is omitted.