A Unified Framework for Motion Estimation and Online Camera Orientation Calibration Using a Ground-View Event Camera

Accurate motion estimation and online camera orientation calibration are important for onboard vision systems on ground vehicles. Ground-view sensing is attractive for these tasks because the ground is typically static, close to the vehicle, and continuously observable, but fast ground-relative motion requires high-temporal-resolution sensing. This paper presents a unified framework that uses a ground-view event camera to estimate ground-view camera motion and support online orientation calibration of a target vehicle-mounted camera. The event camera provides high temporal resolution with sparse measurements, making it suitable for fast ground-view sensing. The framework couples online event-based ground-view motion estimation with an improved calibration process, allowing the estimated motion to support calibration and be further refined during calibration. Specifically, ground-view motion is first estimated online from short event windows using a spiking neural network, providing direct short-term motion information from the event stream. The estimated motion is then integrated with target-camera motion and geometric constraints to estimate the target-camera orientation while further refining the ground-view motion. Experiments on real-vehicle data show accurate ground-view motion estimation, improved rotational components through motion refinement, and better target-camera orientation calibration, especially in yaw, while improvements in pitch and roll are less pronounced. These results demonstrate the effectiveness of integrating event-camera-based ground-view motion estimation and online target-camera orientation calibration for ground vehicles.