Abstract
Light Detection and Ranging (LiDAR) is a sensor that uses a laser to represent the surrounding environment in three-dimensional information. Thanks to the development of LiDAR, LiDAR-based applications are being actively used in autonomous vehicles. In order to effectively use the information coming from LiDAR, extrinsic calibration which finds the translation and the rotation relationship between LiDAR coordinate and vehicle coordinate is essential. Therefore, many studies on LiDAR extrinsic calibration are steadily in progress. The performance index (PI) of the calibration parameter is a value that quantitatively indicates whether the obtained calibration parameter is similar to the true value or not. In order to effectively use the obtained calibration parameter, it is important to validate the parameter through PI. Therefore, in this paper, we propose an algorithm to obtain the performance index for the calibration parameter between LiDAR and the motion sensor. This performance index is experimentally verified in various environments by Monte Carlo simulation and validated using CarMaker simulation data and real data. As a result of verification, the PI of the calibration parameter obtained through the proposed algorithm has the smallest value when the calibration parameter has a true value, and increases as an error is added to the true value. In other words, it has been proven that PI is convex to the calibration parameter. In addition, it is able to confirm that the PI obtained using the proposed algorithm provides information on the effect of the calibration parameters on mapping and localization.
Highlights
Academic Editors: Javier AlonsoAutonomous vehicles combine information from various sensors, such as light detection and ranging (LiDAR), cameras, Global Navigation Satellite System (GNSS), and InertialNavigation System (INS), with vehicle data to recognize the surrounding environment and determine the current vehicle status
It can determine how much it influences the localization performance as well as the goodness of fit for extrinsic calibration results. It was experimentally verified through Monte Carlo simulation as to whether it is effective in various environments
Tmotion lidar composed of a formation matrix composed of 6-DoF pose of motion sensor, and Tmotion calibration parameter are obtained through Monte Carlo sampling
Summary
Autonomous vehicles combine information from various sensors, such as light detection and ranging (LiDAR), cameras, Global Navigation Satellite System (GNSS), and Inertial. As the measured ground truth is likely to change, it is very difficult to always obtain an accurate ground-truth value Another method is to use standard deviation, repeatability, and convergence as performance indicators after trying indirect calibration several times. It presents a novel algorithm to obtain a performance index for the 6-DoF extrinsic calibration parameter between LiDAR and the motion sensor based on localization and mapping. It can determine how much it influences the localization performance as well as the goodness of fit for extrinsic calibration results.
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