Low-Cost Calibration of Matching Error between Lidar and Motor for a Rotating 2D Lidar

Abstract

For a rotating 2D lidar, the inaccurate matching between the 2D lidar and the motor is an important error resource of the 3D point cloud, where the error is shown both in shape and attitude. Existing methods need to measure the angle position of the motor shaft in real time to synchronize the 2D lidar data and the motor shaft angle. However, the sensor used for measurement is usually expensive, which can increase the cost. Therefore, we propose a low-cost method to calibrate the matching error between the 2D lidar and the motor, without using an angular sensor. First, the sequence between the motor and the 2D lidar is optimized to eliminate the shape error of the 3D point cloud. Next, we eliminate the attitude error with uncertainty of the 3D point cloud by installing a triangular plate on the prototype. Finally, the Levenberg–Marquardt method is used to calibrate the installation error of the triangular plate. Experiments verified that the accuracy of our method can meet the requirements of the 3D mapping of indoor autonomous mobile robots. While we use a 2D lidar Hokuyo UST-10LX with an accuracy of ±40 mm in our prototype, we can limit the mapping error within ±50 mm when the distance is no more than 2.2996 m for a 1 s scan (mode 1), and we can limit the mapping error within ±50 mm at the measuring range 10 m for a 16 s scan (mode 7). Our method can reduce the cost while the accuracy is ensured, which can make a rotating 2D lidar cheaper.