scholarly journals Extrinsic LiDAR/Ground Calibration Method Using 3D Geometrical Plane-Based Estimation

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2841
Author(s):  
Mohammad Ali Zaiter ◽  
Régis Lherbier ◽  
Ghaleb Faour ◽  
Oussama Bazzi ◽  
Jean-Charles Noyer
Keyword(s):  
Ground Plane ◽  
Ground Surface ◽  
Real Data ◽  
Calibration Method ◽  
Parameters Estimation ◽  
Parameters Optimization ◽  
Method Performance ◽  
Extrinsic Calibration ◽  
Extrinsic Parameters ◽  
The Stability

This paper details a new extrinsic calibration method for scanning laser rangefinder that is precisely focused on the geometrical ground plane-based estimation. This method is also efficient in the challenging experimental configuration of a high angle of inclination of the LiDAR. In this configuration, the calibration of the LiDAR sensor is a key problem that can be be found in various domains and in particular to guarantee the efficiency of ground surface object detection. The proposed extrinsic calibration method can be summarized by the following procedure steps: fitting ground plane, extrinsic parameters estimation (3D orientation angles and altitude), and extrinsic parameters optimization. Finally, the results are presented in terms of precision and robustness against the variation of LiDAR’s orientation and range accuracy, respectively, showing the stability and the accuracy of the proposed extrinsic calibration method, which was validated through numerical simulation and real data to prove the method performance.

scholarly journals Extrinsic Calibration of 2D Laser Rangefinders Using an Existing Cuboid-Shaped Corridor as the Reference

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4371 ◽  
Author(s):  
Deyu Yin ◽  
Jingbin Liu ◽  
Teng Wu ◽  
Keke Liu ◽  
Juha Hyyppä ◽  
...  
Keyword(s):  
Point Cloud ◽  
Autonomous Systems ◽  
Real Data ◽  
Calibration Method ◽  
Geometric Constraints ◽  
Key Factors ◽  
Extrinsic Calibration ◽  
Localization And Mapping ◽  
Calibration Problem ◽  
Orthogonality Constraint

Laser rangefinders (LRFs) are widely used in autonomous systems for indoor positioning and mobile mapping through the simultaneous localization and mapping (SLAM) approach. The extrinsic parameters of multiple LRFs need to be determined, and they are one of the key factors impacting system performance. This study presents an extrinsic calibration method of multiple LRFs that requires neither extra calibration sensors nor special artificial reference landmarks. Instead, it uses a naturally existing cuboid-shaped corridor as the calibration reference, and it hence needs no additional cost. The present method takes advantage of two types of geometric constraints for the calibration, which can be found in a common cuboid-shaped corridor. First, the corresponding point cloud is scanned by the set of LRFs. Second, the lines that are scanned on the corridor surfaces are extracted from the point cloud. Then, the lines within the same surface and the lines within two adjacent surfaces satisfy the coplanarity constraint and the orthogonality constraint, respectively. As such, the calibration problem is converted into a nonlinear optimization problem with the constraints. Simulation experiments and experiments based on real data verified the feasibility and stability of the proposed method.

scholarly journals A Bio-Inspired Compliance Planning and Implementation Method for Hydraulically Actuated Quadruped Robots with Consideration of Ground Stiffness

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2838
Author(s):  
Xiaoxing Zhang ◽  
Haoyuan Yi ◽  
Junjun Liu ◽  
Qi Li ◽  
Xin Luo
Keyword(s):  
Harmonic Oscillation ◽  
Ground Surface ◽  
Legged Locomotion ◽  
Soft Ground ◽  
Quadruped Robots ◽  
In Series ◽  
Reaction Forces ◽  
Implementation Method ◽  
The Stability ◽  
Natural Dynamics

There has been a rising interest in compliant legged locomotion to improve the adaptability and energy efficiency of robots. However, few approaches can be generalized to soft ground due to the lack of consideration of the ground surface. When a robot locomotes on soft ground, the elastic robot legs and compressible ground surface are connected in series. The combined compliance of the leg and surface determines the natural dynamics of the whole system and affects the stability and efficiency of the robot. This paper proposes a bio-inspired leg compliance planning and implementation method with consideration of the ground surface. The ground stiffness is estimated based on analysis of ground reaction forces in the frequency domain, and the leg compliance is actively regulated during locomotion, adapting them to achieve harmonic oscillation. The leg compliance is planned on the condition of resonant movement which agrees with natural dynamics and facilitates rhythmicity and efficiency. The proposed method has been implemented on a hydraulic quadruped robot. The simulations and experimental results verified the effectiveness of our method.

scholarly journals Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target

2021 ◽  
Vol 11 (2) ◽  
pp. 582
Author(s):  
Zean Bu ◽  
Changku Sun ◽  
Peng Wang ◽  
Hang Dong
Keyword(s):  
Simulated Data ◽  
Real Data ◽  
Calibration Method ◽  
Multiple Sensors ◽  
Triangular Pyramid ◽  
World Coordinate System ◽  
Flash Lidar ◽  
Novel Method ◽  
3D Information ◽  
Incremental Validation

Calibration between multiple sensors is a fundamental procedure for data fusion. To address the problems of large errors and tedious operation, we present a novel method to conduct the calibration between light detection and ranging (LiDAR) and camera. We invent a calibration target, which is an arbitrary triangular pyramid with three chessboard patterns on its three planes. The target contains both 3D information and 2D information, which can be utilized to obtain intrinsic parameters of the camera and extrinsic parameters of the system. In the proposed method, the world coordinate system is established through the triangular pyramid. We extract the equations of triangular pyramid planes to find the relative transformation between two sensors. One capture of camera and LiDAR is sufficient for calibration, and errors are reduced by minimizing the distance between points and planes. Furthermore, the accuracy can be increased by more captures. We carried out experiments on simulated data with varying degrees of noise and numbers of frames. Finally, the calibration results were verified by real data through incremental validation and analyzing the root mean square error (RMSE), demonstrating that our calibration method is robust and provides state-of-the-art performance.

Stable Calibrations of Six-DOF Serial Robots by Using Identification Models with Equalized Singular Values

Robotica ◽  
2021 ◽  
pp. 1-22
Author(s):  
Zhouxiang Jiang ◽  
Min Huang
Keyword(s):  
Industrial Robot ◽  
Singular Values ◽  
Calibration Method ◽  
Identification Accuracy ◽  
Condition Numbers ◽  
Measurement Instruments ◽  
Unknown Parameters ◽  
Calibration Methods ◽  
Six Dof ◽  
The Stability

SUMMARY In typical calibration methods (kinematic or non-kinematic) for serial industrial robot, though measurement instruments with high resolutions are adopted, measurement configurations are optimized, and redundant parameters are eliminated from identification model, calibration accuracy is still limited under measurement noise. This might be because huge gaps still exist among the singular values of typical identification Jacobians, thereby causing the identification models ill conditioned. This paper addresses such problem by using new identification models established in two steps. First, the typical models are divided into the submodels with truncated singular values. In this way, the unknown parameters corresponding to the abnormal singular values are removed, thereby reducing the condition numbers of the new submodels. However, these models might still be ill conditioned. Therefore, the second step is to further centralize the singular values of each submodel by using a matrix balance method. Afterward, all submodels are well conditioned and obtain much higher observability indices compared with those of typical models. Simulation results indicate that significant improvements in the stability of identification results and the identifiability of unknown parameters are acquired by using the new identification submodels. Experimental results indicate that the proposed calibration method increases the identification accuracy without incurring additional hardware setup costs to the typical calibration method.

Contrôle de la stabilité des remblais par la mesure des déplacements horizontaux

1974 ◽  
Vol 11 (1) ◽  
pp. 182-201 ◽  
Author(s):  
René Marche ◽  
Robert Chapuis
Keyword(s):  
Factor Of Safety ◽  
Soft Clay ◽  
Ground Surface ◽  
Soft Layer ◽  
The Stability ◽  
Horizontal Displacements

The horizontal displacements measured at the toe of eight embankments are analyzed as a function of the factor of safety. The embankments are built on layers of soft clay. Only the undrained stage is studied.When the factor of safety of the embankments is higher than about 1.4, the horizontal displacements on the ground surface, at the toe of the embankment seem to follow an elastic law which is highly dependent on the ratio of the thickness of the soft layer to the width of the embankment. When the factor of safety is lower than about 1.4, the horizontal displacements do not follow an elastic law, they increase considerably. Consequently, it is suggested that the horizontal displacements be precisely measured at the toe of embankments during construction. These measurements are simple and sensitive to the approach of failure, they can be efficiently used to control the stability of embankments. This study also gives some information concerning the variation of horizontal displacements versus depth.

scholarly journals Extrinsic Parameters Calibration Method of Cameras with Non-Overlapping Fields of View in Airborne Remote Sensing

2018 ◽  
Vol 10 (8) ◽  
pp. 1298 ◽  
Author(s):  
Lei Yin ◽  
Xiangjun Wang ◽  
Yubo Ni ◽  
Kai Zhou ◽  
Jilong Zhang
Keyword(s):  
Remote Sensing ◽  
Camera Calibration ◽  
Calibration Method ◽  
Optimization Method ◽  
Airborne Remote Sensing ◽  
Camera System ◽  
Translation Error ◽  
Camera Systems ◽  
Calibration Methods ◽  
Extrinsic Parameters

Multi-camera systems are widely used in the fields of airborne remote sensing and unmanned aerial vehicle imaging. The measurement precision of these systems depends on the accuracy of the extrinsic parameters. Therefore, it is important to accurately calibrate the extrinsic parameters between the onboard cameras. Unlike conventional multi-camera calibration methods with a common field of view (FOV), multi-camera calibration without overlapping FOVs has certain difficulties. In this paper, we propose a calibration method for a multi-camera system without common FOVs, which is used on aero photogrammetry. First, the extrinsic parameters of any two cameras in a multi-camera system is calibrated, and the extrinsic matrix is optimized by the re-projection error. Then, the extrinsic parameters of each camera are unified to the system reference coordinate system by using the global optimization method. A simulation experiment and a physical verification experiment are designed for the theoretical arithmetic. The experimental results show that this method is operable. The rotation error angle of the camera’s extrinsic parameters is less than 0.001rad and the translation error is less than 0.08 mm.

scholarly journals Constrained Multiple Planar Reconstruction for Automatic Camera Calibration of Intelligent Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4643
Author(s):  
Sang Jun Lee ◽  
Jeawoo Lee ◽  
Wonju Lee ◽  
Cheolhun Jang
Keyword(s):  
Camera Calibration ◽  
Calibration Method ◽  
Image Features ◽  
Intelligent Vehicles ◽  
Point Problem ◽  
Reconstruction Method ◽  
Calibration Methods ◽  
The World ◽  
Extrinsic Parameters ◽  
Automatic Camera Calibration

In intelligent vehicles, extrinsic camera calibration is preferable to be conducted on a regular basis to deal with unpredictable mechanical changes or variations on weight load distribution. Specifically, high-precision extrinsic parameters between the camera coordinate and the world coordinate are essential to implement high-level functions in intelligent vehicles such as distance estimation and lane departure warning. However, conventional calibration methods, which solve a Perspective-n-Point problem, require laborious work to measure the positions of 3D points in the world coordinate. To reduce this inconvenience, this paper proposes an automatic camera calibration method based on 3D reconstruction. The main contribution of this paper is a novel reconstruction method to recover 3D points on planes perpendicular to the ground. The proposed method jointly optimizes reprojection errors of image features projected from multiple planar surfaces, and finally, it significantly reduces errors in camera extrinsic parameters. Experiments were conducted in synthetic simulation and real calibration environments to demonstrate the effectiveness of the proposed method.

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