Static Extrinsic Calibration of a Vehicle-Mounted Lidar Using Spherical Targets

Abstract: Self-driving cars are steadily becoming a reality by a growing number of driver assistance functions enabled by smart perception sensors. The light detection and ranging (lidar) sensor show great potential for perception tasks due to its precise distance measurements. In order to take advantage of the high precision of a vehicle-mounted lidar, its position relative the vehicle needs to be calibrated. This is known as extrinsic calibration. The aim of this thesis is to investigate how to perform the extrinsic calibration of a static vehicle-mounted lidar in a static environment. In addition, the aim has been to develop a tool for running and customizing calibration simulations. The simulation tool CARLA, with its Python application programming interface (API), was chosen and developed to perform lidar simulations in a created virtual factory environment. The chosen calibration method uses a single vehicle-mounted lidar, targeting three spherical targets, whose known centre points act as reference points for the calibration. From the lidar point cloud a calibration algorithm is applied to find the position of the lidar. The algorithm estimates the centre points of the spherical targets and finds the lidar position by aligning the estimated centre points with the reference centre points. The algorithm includes functions that preprocess, cluster, fit spheres and perform point-to-point iterative closest point (ICP). The calibration method showed promising results in terms of point alignment and lidar position estimations. From 1000 simulations of random lidar positions, the average root mean square error (RMSE) of the point alignment was 0.33 mm with a standard deviation of 0.091 mm. The average absolute error of lidar position estimations was for translation [1.0 mm, 0.40 mm, 2.0 mm] with standard deviation [0.20 mm, 0.29 mm, 0.58 mm], and for rotation [0.11°, 0.11°, 0.10°] with standard deviation [0.098°, 0.098°, 0.094°]. Results also showed that uncertainties in the form of noise and point density have an impact on the accuracy of the calibration method. The developed simulation tool in CARLA can be used by other engineers at Veoneer to run customized calibrations or investigate other autonomous driving applications. Another conclusion is that the calibration method provides fast and accurate computations making it a potential candidate for extrinsic calibration.