scholarly journals AUTOMATIC IN-SITU SELF-CALIBRATION OF A PANORAMIC TLS FROM A SINGLE STATION USING 2D KEYPOINTS

2019 ◽  
Vol IV-2/W5 ◽  
pp. 413-420
Author(s):  
T. Medić ◽  
H. Kuhlmann ◽  
C. Holst
Keyword(s):  
Real Data ◽  
Point Clouds ◽  
Calibration Method ◽  
Evaluation Procedure ◽  
Random Errors ◽  
Self Calibration ◽  
Single Station ◽  
The Impact ◽  
Calibration Parameters

<p><strong>Abstract.</strong> Terrestrial laser scanner (TLS) measurements are unavoidably affected by systematic influences due to internal misalignments. The magnitude of the resulting errors can exceed the magnitude of random errors significantly deteriorating the quality of the obtained point clouds. Hence, the task of calibrating TLSs is important for applications with high demands regarding accuracy. In recent years, multiple in-situ self-calibration approaches were derived allowing the successful estimation of up-to-date calibration parameters. These approaches rely either on using manually placed targets or on using man-made geometric objects found in surroundings. Herein, we widen the existing toolbox with an alternative approach for panoramic TLSs, for the cases where such prerequisites cannot be met. We build upon the existing target-based two-face calibration method by substituting targets with precisely localized 2D keypoints, i.e. local features, detected in panoramic intensity images using the Förstner operator. To overcome the detriment of the perspective change on the feature localization accuracy, we estimate the majority of the relevant calibration parameters from a single station. The approach is verified on real data obtained with the Leica ScanStation P20. The obtained results were tested against the affirmed target-based two-face self-calibration. Analysis proved that the estimated calibration parameters are directly comparable both in the terms of parameter precision and correlation. In the end, we employ an effective evaluation procedure for testing the impact of the calibration results on the point cloud quality.</p>

Obstacle detection based on depth fusion of lidar and radar in challenging conditions

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Guotao Xie ◽  
Jing Zhang ◽  
Junfeng Tang ◽  
Hongfei Zhao ◽  
Ning Sun ◽  
...  
Keyword(s):  
False Alarm ◽  
Experimental Tests ◽  
Point Clouds ◽  
Calibration Method ◽  
Obstacle Detection ◽  
Environmental Perception ◽  
Fusion Model ◽  
Content Type ◽  
Self Calibration ◽  
Temporal Relationships

Purpose To the industrial application of intelligent and connected vehicles (ICVs), the robustness and accuracy of environmental perception are critical in challenging conditions. However, the accuracy of perception is closely related to the performance of sensors configured on the vehicle. To enhance sensors’ performance further to improve the accuracy of environmental perception, this paper aims to introduce an obstacle detection method based on the depth fusion of lidar and radar in challenging conditions, which could reduce the false rate resulting from sensors’ misdetection. Design/methodology/approach Firstly, a multi-layer self-calibration method is proposed based on the spatial and temporal relationships. Next, a depth fusion model is proposed to improve the performance of obstacle detection in challenging conditions. Finally, the study tests are carried out in challenging conditions, including straight unstructured road, unstructured road with rough surface and unstructured road with heavy dust or mist. Findings The experimental tests in challenging conditions demonstrate that the depth fusion model, comparing with the use of a single sensor, can filter out the false alarm of radar and point clouds of dust or mist received by lidar. So, the accuracy of objects detection is also improved under challenging conditions. Originality/value A multi-layer self-calibration method is conducive to improve the accuracy of the calibration and reduce the workload of manual calibration. Next, a depth fusion model based on lidar and radar can effectively get high precision by way of filtering out the false alarm of radar and point clouds of dust or mist received by lidar, which could improve ICVs’ performance in challenging conditions.

scholarly journals Rigorous Boresight Self-Calibration of Mobile and UAV LiDAR Scanning Systems by Strip Adjustment

2019 ◽  
Vol 11 (4) ◽  
pp. 442 ◽  
Author(s):  
Zhen Li ◽  
Junxiang Tan ◽  
Hua Liu
Keyword(s):  
Inertial Measurement Unit ◽  
Point Clouds ◽  
Calibration Method ◽  
Satellite System ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Self Calibration ◽  
Global Navigation Satellite ◽  
Mean Square Errors ◽  
Scanning Systems

Mobile LiDAR Scanning (MLS) systems and UAV LiDAR Scanning (ULS) systems equipped with precise Global Navigation Satellite System (GNSS)/Inertial Measurement Unit (IMU) positioning units and LiDAR sensors are used at an increasing rate for the acquisition of high density and high accuracy point clouds because of their safety and efficiency. Without careful calibration of the boresight angles of the MLS systems and ULS systems, the accuracy of data acquired would degrade severely. This paper proposes an automatic boresight self-calibration method for the MLS systems and ULS systems using acquired multi-strip point clouds. The boresight angles of MLS systems and ULS systems are expressed in the direct geo-referencing equation and corrected by minimizing the misalignments between points scanned from different directions and different strips. Two datasets scanned by MLS systems and two datasets scanned by ULS systems were used to verify the proposed boresight calibration method. The experimental results show that the root mean square errors (RMSE) of misalignments between point correspondences of the four datasets after boresight calibration are 2.1 cm, 3.4 cm, 5.4 cm, and 6.1 cm, respectively, which are reduced by 59.6%, 75.4%, 78.0%, and 94.8% compared with those before boresight calibration.

scholarly journals A General Point-Based Method for Self-Calibration of Terrestrial Laser Scanners Considering Stochastic Information

2020 ◽  
Vol 12 (18) ◽  
pp. 2923
Author(s):  
Tengfei Zhou ◽  
Xiaojun Cheng ◽  
Peng Lin ◽  
Zhenlun Wu ◽  
Ensheng Liu
Keyword(s):  
Point Cloud ◽  
A Priori ◽  
Real Data ◽  
Point Clouds ◽  
Estimation Algorithm ◽  
General Point ◽  
Exterior Orientation ◽  
Self Calibration ◽  
Laser Scanners ◽  
Orientation Parameters

Due to the existence of environmental or human factors, and because of the instrument itself, there are many uncertainties in point clouds, which directly affect the data quality and the accuracy of subsequent processing, such as point cloud segmentation, 3D modeling, etc. In this paper, to address this problem, stochastic information of point cloud coordinates is taken into account, and on the basis of the scanner observation principle within the Gauss–Helmert model, a novel general point-based self-calibration method is developed for terrestrial laser scanners, incorporating both five additional parameters and six exterior orientation parameters. For cases where the instrument accuracy is different from the nominal ones, the variance component estimation algorithm is implemented for reweighting the outliers after the residual errors of observations obtained. Considering that the proposed method essentially is a nonlinear model, the Gauss–Newton iteration method is applied to derive the solutions of additional parameters and exterior orientation parameters. We conducted experiments using simulated and real data and compared them with those two existing methods. The experimental results showed that the proposed method could improve the point accuracy from 10−4 to 10−8 (a priori known) and 10−7 (a priori unknown), and reduced the correlation among the parameters (approximately 60% of volume). However, it is undeniable that some correlations increased instead, which is the limitation of the general method.

scholarly journals Analysis and Self-Calibration Method for Asynchrony between Sensors in Rotation INS

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2921 ◽  
Author(s):  
Jie Sui ◽  
Lei Wang ◽  
Tao Huang ◽  
Qi Zhou
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Calibration Method ◽  
Velocity Error ◽  
Self Calibration ◽  
Axis Rotation ◽  
The Impact ◽  
Navigation Errors

The gyroscope, accelerometer and angular encoder are the most important components in a dual-axis rotation inertial navigation system (RINS). However, there are asynchronies among the sensors, which will thus lead to navigation errors. The impact of asynchrony between the gyroscope and angular encoder on the azimuth error and the impact of asynchrony between the gyroscope and accelerometer on the velocity error are analyzed in this paper. A self-calibration method based on navigation errors is proposed based on the analysis above. Experiments show that azimuth and velocity accuracy can be improved by compensating the asynchronies.

scholarly journals POINT CLOUD REFINEMENT WITH A TARGET-FREE INTRINSIC CALIBRATION OF A MOBILE MULTI-BEAM LIDAR SYSTEM

2016 ◽  
Vol XLI-B3 ◽  
pp. 359-366
Author(s):  
H. Nouiraa ◽  
J. E. Deschaud ◽  
F. Goulettea
Keyword(s):  
Reference Frame ◽  
Real Data ◽  
Point Clouds ◽  
The Body ◽  
Mobile Mapping ◽  
Extrinsic Calibration ◽  
Body Reference ◽  
3D Information ◽  
Calibration Parameters ◽  
Mapping Systems

LIDAR sensors are widely used in mobile mapping systems. The mobile mapping platforms allow to have fast acquisition in cities for example, which would take much longer with static mapping systems. The LIDAR sensors provide reliable and precise 3D information, which can be used in various applications: mapping of the environment; localization of objects; detection of changes. Also, with the recent developments, multi-beam LIDAR sensors have appeared, and are able to provide a high amount of data with a high level of detail. &lt;br&gt;&lt;br&gt; A mono-beam LIDAR sensor mounted on a mobile platform will have an extrinsic calibration to be done, so the data acquired and registered in the sensor reference frame can be represented in the body reference frame, modeling the mobile system. For a multibeam LIDAR sensor, we can separate its calibration into two distinct parts: on one hand, we have an extrinsic calibration, in common with mono-beam LIDAR sensors, which gives the transformation between the sensor cartesian reference frame and the body reference frame. On the other hand, there is an intrinsic calibration, which gives the relations between the beams of the multi-beam sensor. This calibration depends on a model given by the constructor, but the model can be non optimal, which would bring errors and noise into the acquired point clouds. In the litterature, some optimizations of the calibration parameters are proposed, but need a specific routine or environment, which can be constraining and time-consuming. &lt;br&gt;&lt;br&gt; In this article, we present an automatic method for improving the intrinsic calibration of a multi-beam LIDAR sensor, the Velodyne HDL-32E. The proposed approach does not need any calibration target, and only uses information from the acquired point clouds, which makes it simple and fast to use. Also, a corrected model for the Velodyne sensor is proposed. &lt;br&gt;&lt;br&gt; An energy function which penalizes points far from local planar surfaces is used to optimize the different proposed parameters for the corrected model, and we are able to give a confidence value for the calibration parameters found. Optimization results on both synthetic and real data are presented.

An in-situ self-calibration method for non-contact full-field strain measurement

Measurement ◽  
2020 ◽  
Vol 162 ◽  
pp. 107871
Author(s):  
Huaxia Deng ◽  
Jun Wang ◽  
Haicong Zhang ◽  
Jin Zhang ◽  
Mengchao Ma ◽  
...  
Keyword(s):  
Strain Measurement ◽  
Calibration Method ◽  
Field Strain ◽  
Full Field ◽  
Self Calibration

A Simplified Self-Calibration Method for Cylindrical Grating

2017 ◽  
Vol 868 ◽  
pp. 99-104
Author(s):  
Wen Ying Zhang ◽  
Wei Hu Zhou ◽  
Da Bao Lao ◽  
Hao Ran Zhu
Keyword(s):  
High Precision ◽  
Calibration Method ◽  
Relative Displacement ◽  
Angle Measurement ◽  
Random Errors ◽  
Angle Sensor ◽  
Measuring Device ◽  
Self Calibration ◽  
Angle Measuring ◽  
System Errors

Cylindrical grating angle sensor is a common angle measuring device with high precision. Due to the reflecting arc surface of the cylindrical grating the effects of relative displacement of graduation line and gap variation will interact. Significant residual errors still exist even with high precision cylindrical grating and multiple reading heads around the scale disc .In this paper, based on the principle of circumference closure, the principle of self-calibration is deduced and analyzed in detail. The calibration curve is obtained by obtaining the deviation between the certain position and an ideal position of the graduation line position in real time. A simple self-calibration method of cylindrical grating is acquired. It can calibrate random errors and system errors with time and improve the angle measurement accuracy. The method has the advantages of simple structure and strong maneuverability, and provides technical means for the miniaturization and high precision of cylindrical grating.

scholarly journals Six-Axis Force Torque Sensor Model-Based In Situ Calibration Method and Its Impact in Floating-Based Robot Dynamic Performance

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5521
Author(s):  
Francisco Javier Andrade Chavez ◽  
Silvio Traversaro ◽  
Daniele Pucci
Keyword(s):  
Dynamic Performance ◽  
Calibration Method ◽  
Full Description ◽  
Temperature Drift ◽  
Model Based ◽  
Floating Base ◽  
In Situ Calibration ◽  
Calibration Methods ◽  
The Impact

A crucial part of dynamic motions is the interaction with other objects or the environment. Floating base robots have yet to perform these motions repeatably and reliably. Force torque sensors are able to provide the full description of a contact. Despite that, their use beyond a simple threshold logic is not widespread in floating base robots. Force torque sensors might change performance when mounted, which is why in situ calibration methods can improve the performance of robots by ensuring better force torque measurements. The Model-Based in situ calibration method with temperature compensation has shown promising results in improving FT sensor measurements. There are two main goals for this paper. The first is to facilitate the use and understanding of the method by providing guidelines that show their usefulness through experimental results. Then the impact of having better FT measurements with no temperature drift are demonstrated by proving that the offset estimated with this method is still useful days and even a month from the time of estimation. The effect of this is showcased by comparing the sensor response with different offsets simultaneously during real robot experiments. Furthermore, quantitative results of the improvement in dynamic behaviors due to the in situ calibration are shown. Finally, we show how using better FT measurements as feedback in low and high level controllers can impact the performance of floating base robots during dynamic motions. Experiments were performed on the floating base robot iCub.

Sign in / Sign up

Export Citation Format

Share Document