Motion-based extrinsic parameter calibration of a robot’s multisensor system

Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto. fi Author Matti Pekkanen Title Motion-based extrinsic parameter calibration of a robot’s multisensor system School School of Electrical Engineering Master’s programme Automation and Electrical Engineering Major Control, Robotics and Autonomous Systems Code ELEC3025 Supervisor Prof. Arto Visala Advisor D. Sc. (Tech.) Juhana Ahtiainen Level Master’s thesis Date 23 May 2018 Pages 10+54 Language English Abstract This work presents an extrinsic parameter calibration of two Light Detection And Ranging (LiDAR) sensors fitted in a mobile robot platform. The LiDARs do not see each other, and are connected to separate computers. The calibration method is motion-based, targetless, data driven and requires no a priori information about the system. The motivation of the work comes from the need of a robust and accurate method for extrinsic parameter calibration, that is not dependent on specific calibration location, calibration target, or any a priori information about the system itself. This makes the calibration method usable in situations, where the system to be calibrated cannot easily be moved to a specific calibration location, or the calibration targets are infeasible to move in the sight of the sensors. The initial guesses for the parameters can be hard to obtain if the system is large, or the sensors hard to reach. The method described in this work supports multimodal sensors, and is not restricted to using LiDAR sensors. The extrinsic parameters of the LiDARs sensors are estimated in relation to the robot’s base link. First, the base link trajectory is estimated using an extended Kalman filterbased algorithm that uses an Real-Time Kinematic (RTK) GNSS, an Inertial Measurement Unit (IMU) and wheel encoders. Second, the LiDAR trajectories are estimated using a normal distributions transform (NDT)-based registration approach. The time offset between the two computers is estimated from the estimated rotational magnitudes, and outliers are filtered from the data. Finally, the data are sampled to provide pose pairs, from which the extrinsic parameters are estimated using a cascading optimization, where a genetic algorithm optimizes the initial values of a gradient descent algorithm, and uses the output of the gradient descent algorithm as its cost function. The gradient descent algorithm optimizes the extrinsic parameters using the difference of the changes of the pose estimates as its cost function. The estimation accuracy is comparable with other motion-based calibration methods. The rotation parameter estimates are accurate, whereas the translation parameter estimates are coarse. This problem is solved by using the measured values for the translation parameters. The results are found to be roughly in line with the state-of-the-art motionbased calibration methods.This work presents an extrinsic parameter calibration of two Light Detection And Ranging (LiDAR) sensors fitted in a mobile robot platform. The LiDARs do not see each other, and are connected to separate computers. The calibration method is motion-based, targetless, data driven and requires no a priori information about the system. The motivation of the work comes from the need of a robust and accurate method for extrinsic parameter calibration, that is not dependent on specific calibration location, calibration target, or any a priori information about the system itself. This makes the calibration method usable in situations, where the system to be calibrated cannot easily be moved to a specific calibration location, or the calibration targets are infeasible to move in the sight of the sensors. The initial guesses for the parameters can be hard to obtain if the system is large, or the sensors hard to reach. The method described in this work supports multimodal sensors, and is not restricted to using LiDAR sensors. The extrinsic parameters of the LiDARs sensors are estimated in relation to the robot’s base link. First, the base link trajectory is estimated using an extended Kalman filterbased algorithm that uses an Real-Time Kinematic (RTK) GNSS, an Inertial Measurement Unit (IMU) and wheel encoders. Second, the LiDAR trajectories are estimated using a normal distributions transform (NDT)-based registration approach. The time offset between the two computers is estimated from the estimated rotational magnitudes, and outliers are filtered from the data. Finally, the data are sampled to provide pose pairs, from which the extrinsic parameters are estimated using a cascading optimization, where a genetic algorithm optimizes the initial values of a gradient descent algorithm, and uses the output of the gradient descent algorithm as its cost function. The gradient descent algorithm optimizes the extrinsic parameters using the difference of the changes of the pose estimates as its cost function. The estimation accuracy is comparable with other motion-based calibration methods. The rotation parameter estimates are accurate, whereas the translation parameter estimates are coarse. This problem is solved by using the measured values for the translation parameters. The results are found to be roughly in line with the state-of-the-art motionbased calibration methods.