Extrinsic Camera Calibration with Semantic Segmentation
This work addresses the need for efficient calibration in intelligent vehicles and traffic infrastructure, though it is incremental as it builds on existing lidar and segmentation methods.
The paper tackles the problem of time-consuming and manual extrinsic camera calibration by automating parameter estimation using semantic segmentation from images and point clouds, achieving low error measurements in both simulated and real-world evaluations.
Monocular camera sensors are vital to intelligent vehicle operation and automated driving assistance and are also heavily employed in traffic control infrastructure. Calibrating the monocular camera, though, is time-consuming and often requires significant manual intervention. In this work, we present an extrinsic camera calibration approach that automatizes the parameter estimation by utilizing semantic segmentation information from images and point clouds. Our approach relies on a coarse initial measurement of the camera pose and builds on lidar sensors mounted on a vehicle with high-precision localization to capture a point cloud of the camera environment. Afterward, a mapping between the camera and world coordinate spaces is obtained by performing a lidar-to-camera registration of the semantically segmented sensor data. We evaluate our method on simulated and real-world data to demonstrate low error measurements in the calibration results. Our approach is suitable for infrastructure sensors as well as vehicle sensors, while it does not require motion of the camera platform.