Online cross-calibration of camera and LIDAR

In an autonomous driving system, drift can affect the sensor's position, introducing errors in the extrinsic calibration. For this reason, we have developed a method which continuously monitors two sensors, camera, and LIDAR with 16 beams, and adjusts the value of their cross-calibration. Our algorithm, starting from correct values of the extrinsic cross-calibration parameters, can detect small sensor drift during vehicle driving, by overlapping the edges from the LIDAR over the edges from the image. The novelty of our method is that in order to obtain edges, we create a range image and filter the data from the 3D point cloud, and we use distance transform on 2D images to find edges. Another improvement we bring is applying motion correction on laser scanner data to remove distortions that appear during vehicle motion. An optimization problem on the 6 calibration parameters is defined, from which we are able to obtain the best value of the cross-calibration, and readjust it automatically. Our system performs successfully in real time, in a wide variety of scenarios, and is not affected by the speed of the car.