Rotation and Temporal Calibration of Event Camera and IMU Based on Contrast Maximization

In recent years, camera-IMU sensor suite has garnered significant attention in robotics, augmented reality (AR), and self-driving communities due to its lightweight design and compact footprint. However, traditional frame-based cameras exhibit inherent limitations such as sensing latency and low dynamic range, motivating us to incorporate novel event cameras for enhanced completeness and robustness. As a prerequisite for robust motion estimation, existing event camera-IMU calibration methods generally rely on recovered standard frame images and artificial targets to facilitate spatiotemporal calibration, rendering them not flexible enough and labor-intensive. To address this issue, we propose a targetless rotational and temporal calibration method for event camera and IMU based on contrast maximization. This method directly leverages raw asynchronous event streams without the necessity for complicated manual initialization or prior knowledge. Specifically, a sliding window mechanism is first designed to estimate event camera angular velocity within a short time period. Based on the continuous-time representation, the IMU gyroscope and event camera angular velocity factors are jointly minimized to optimize the spatiotemporal parameters. The real-world experiments conducted in both public and self-collected datasets demonstrate that the proposed method can achieve comparable calibration accuracy against state-of-the-art target-based calibration methods. To benefit the research community, we open source the code and collected data at https://github.com/GREAT-WHU/RTEI-Calib