  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.
