scholarly journals Optimization-Based Online Initialization and Calibration of Monocular Visual-Inertial Odometry Considering Spatial-Temporal Constraints

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2673
Author(s):  
Weibo Huang ◽  
Weiwei Wan ◽  
Hong Liu
Keyword(s):  
Measurement Data ◽  
Calibration Method ◽  
Bundle Adjustment ◽  
Measurement Unit ◽  
Temporal Parameters ◽  
Work Routine ◽  
Initial States ◽  
Time Offset ◽  
Public Datasets ◽  
Metric Scale

The online system state initialization and simultaneous spatial-temporal calibration are critical for monocular Visual-Inertial Odometry (VIO) since these parameters are either not well provided or even unknown. Although impressive performance has been achieved, most of the existing methods are designed for filter-based VIOs. For the optimization-based VIOs, there is not much online spatial-temporal calibration method in the literature. In this paper, we propose an optimization-based online initialization and spatial-temporal calibration method for VIO. The method does not need any prior knowledge about spatial and temporal configurations. It estimates the initial states of metric-scale, velocity, gravity, Inertial Measurement Unit (IMU) biases, and calibrates the coordinate transformation and time offsets between the camera and IMU sensors. The work routine of the method is as follows. First, it uses a time offset model and two short-term motion interpolation algorithms to align and interpolate the camera and IMU measurement data. Then, the aligned and interpolated results are sent to an incremental estimator to estimate the initial states and the spatial–temporal parameters. After that, a bundle adjustment is additionally included to improve the accuracy of the estimated results. Experiments using both synthetic and public datasets are performed to examine the performance of the proposed method. The results show that both the initial states and the spatial-temporal parameters can be well estimated. The method outperforms other contemporary methods used for comparison.

scholarly journals Accurate Calibration of a Self-Developed Vehicle-Borne LiDAR Scanning System

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Ming Guo ◽  
Bingnan Yan ◽  
Tengfei Zhou ◽  
Deng Pan ◽  
Guoli Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Measurement Data ◽  
Point Clouds ◽  
Calibration Method ◽  
Linear Interpolation ◽  
Measurement Unit ◽  
Scanning System ◽  
High Precision Measurement ◽  
Distance Ratio

To obtain high-precision measurement data using vehicle-borne light detection and ranging (LiDAR) scanning (VLS) systems, calibration is necessary before a data acquisition mission. Thus, a novel calibration method based on a homemade target ball is proposed to estimate the system mounting parameters, which refer to the rotational and translational offsets between the LiDAR sensor and inertial measurement unit (IMU) orientation and position. Firstly, the spherical point cloud is fitted into a sphere to extract the coordinates of the centre, and each scan line on the sphere is fitted into a section of the sphere to calculate the distance ratio from the centre to the nearest two sections, and the attitude and trajectory parameters of the centre are calculated by linear interpolation. Then, the real coordinates of the centre of the sphere are calculated by measuring the coordinates of the reflector directly above the target ball with the total station. Finally, three rotation parameters and three translation parameters are calculated by two least-squares adjustments. Comparisons of the point cloud before and after calibration and the calibrated point clouds with the point cloud obtained by the terrestrial laser scanner show that the accuracy significantly improved after calibration. The experiment indicates that the calibration method based on the homemade target ball can effectively improve the accuracy of the point cloud, which can promote VLS development and applications.

Systematic Calibration Method for FOG Inertial Measurement Units

2013 ◽  
Vol 662 ◽  
pp. 717-720 ◽  
Author(s):  
Zhen Yu Zheng ◽  
Yan Bin Gao ◽  
Kun Peng He
Keyword(s):  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Calibration Method ◽  
State Equation ◽  
Measurement Unit ◽  
Observation System ◽  
Body Frame ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Error Coefficients

As an inertial sensors assembly, the FOG inertial measurement unit (FIMU) must be calibrated before being used. The paper presents a one-time systematic IMU calibration method only using two-axis low precision turntable. First, the detail error model of inertial sensors using defined body frame is established. Then, only velocity taken as observation, system 33 state equation is established including the lever arm effects and nonlinear terms of scale factor error. The turntable experiments verify that the method can identify all the error coefficients of FIMU on low-precision two-axis turntable, after calibration the accuracy of navigation is improved.

Research on Automatic Joint Calibration Method of Multi 3D-LIDARs and Inertial Measurement Unit

2021 ◽  
Author(s):  
Jinghua Zhang ◽  
Rui He ◽  
Jian Wu ◽  
Shuai Li ◽  
Xuesong Chen ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Calibration Method ◽  
Measurement Unit ◽  
Inertial Measurement

scholarly journals Simultaneous Robot–World and Hand–Eye Calibration without a Calibration Object

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3949 ◽  
Author(s):  
Wei Li ◽  
Mingli Dong ◽  
Naiguang Lu ◽  
Xiaoping Lou ◽  
Peng Sun
Keyword(s):  
Three Dimensional ◽  
Real Data ◽  
Medical Robotics ◽  
Calibration Method ◽  
Bundle Adjustment ◽  
Data Sets ◽  
Rigid Transformation ◽  
Validation Experiment ◽  
Dimensional Reconstruction ◽  
Sparse Bundle Adjustment

An extended robot–world and hand–eye calibration method is proposed in this paper to evaluate the transformation relationship between the camera and robot device. This approach could be performed for mobile or medical robotics applications, where precise, expensive, or unsterile calibration objects, or enough movement space, cannot be made available at the work site. Firstly, a mathematical model is established to formulate the robot-gripper-to-camera rigid transformation and robot-base-to-world rigid transformation using the Kronecker product. Subsequently, a sparse bundle adjustment is introduced for the optimization of robot–world and hand–eye calibration, as well as reconstruction results. Finally, a validation experiment including two kinds of real data sets is designed to demonstrate the effectiveness and accuracy of the proposed approach. The translation relative error of rigid transformation is less than 8/10,000 by a Denso robot in a movement range of 1.3 m × 1.3 m × 1.2 m. The distance measurement mean error after three-dimensional reconstruction is 0.13 mm.

scholarly journals Scalar Method of Fault Diagnosis of Inertial Measurement Unit

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Vadym Avrutov
Keyword(s):  
Fault Diagnosis ◽  
Inertial Measurement Unit ◽  
Calibration Method ◽  
Quality Monitoring ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Working Capacity ◽  
Element Determination ◽  
Inertial Measurement ◽  
Algorithm Verification

The scalar method of fault diagnosis systems of the inertial measurement unit (IMU) is described. All inertial navigation systems consist of such IMU. The scalar calibration method is a base of the scalar method for quality monitoring and diagnostics. In accordance with scalar calibration method algorithms of fault diagnosis systems are developed. As a result of quality monitoring algorithm verification is implemented in the working capacity monitoring of IMU. A failure element determination is based on diagnostics algorithm verification and after that the reason for such failure is cleared. The process of verifications consists of comparison of the calculated estimations of biases, scale factor errors, and misalignments angles of sensors to their data sheet certificate, kept in internal memory of computer. As a result of such comparison the conclusion for working capacity of each IMU sensor can be made and also the failure sensor can be determined.

scholarly journals Thermal Modeling and Calibration Method in Complex Temperature Field for Single-Axis Rotational Inertial Navigation System

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 384 ◽  
Author(s):  
Zihui Wang ◽  
Xianghong Cheng ◽  
Jingjing Du
Keyword(s):  
Temperature Field ◽  
Inertial Navigation ◽  
Calibration Method ◽  
Measurement Unit ◽  
Calibration Model ◽  
Navigation Systems ◽  
Moving Vehicle ◽  
Temperature Change Rate ◽  
Environment Temperature ◽  
Complex Temperature

Single-axis rotational inertial navigation systems (single-axis RINSs) are widely used in high-accuracy navigation because of their ability to restrain the horizontal axis errors of the inertial measurement unit (IMU). The IMU errors, especially the biases, should be constant during each rotation cycle that is to be modulated and restrained. However, the temperature field, consisting of the environment temperature and the power heating of single-axis RINS, affects the IMU performance and changes the biases over time. To improve the precision of single-axis RINS, the change of IMU biases caused by the temperature should be calibrated accurately. The traditional thermal calibration model consists of the temperature and temperature change rate, which does not reflect the complex temperature field of single-axis RINS. This paper proposed a multiple regression method with a temperature gradient in the model, and in order to describe the complex temperature field thoroughly, a BP neural network method is proposed with consideration of the coupled items of the temperature variables. Experiments show that the proposed methods outperform the traditional calibration method. The navigation accuracy of single-axis RINS can be improved by up to 47.41% in lab conditions and 65.11% in the moving vehicle experiment, respectively.

scholarly journals Calibration Method for a Parallel Mechanism Type Machine Tool by Response Surface Methodology –Consideration via Simulation on a Stewart Platform Mechanism–

2010 ◽  
Vol 4 (4) ◽  
pp. 355-363 ◽  
Author(s):  
Hiroshi Yachi ◽  
◽  
Hiroshi Tachiya
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Parallel Mechanism ◽  
Measurement Data ◽  
Calibration Method ◽  
Stewart Platform ◽  
Parallel Mechanisms ◽  
Data Set ◽  
Efficient Data ◽  
Efficient Measurement

This paper proposes a calibration method for parallel mechanisms usingResponse Surface Methodology. This method is a statistical approach to estimating an unknown input-output relationship using a small set of efficient data collected on an intended system. Although identifying locations causing positional errors in a parallel mechanism and precisely measuring the position and posture of the output point are difficult, the proposed calibration method based onResponse Surface Methodologyaims to compensate for positional and postural errors, without indentifying the locations causing these errors, by using a small yet efficient measurement data set. This study analyzes the effectiveness of the method we propose by applying it to a Stewart platform, which is a typical spatial 6-DOF parallel mechanism.

A Rapid Field Calibrating Method for MIMU

2011 ◽  
Vol 80-81 ◽  
pp. 1140-1144
Author(s):  
Yu Bao Fan ◽  
Jie Li ◽  
Bo Wang ◽  
Xiao Chun Tian ◽  
Jun Liu
Keyword(s):  
Inertial Measurement Unit ◽  
Operation Time ◽  
Calibration Method ◽  
Least Square ◽  
Measurement Unit ◽  
Calibration Model ◽  
Time Saving ◽  
Inertial Measurement ◽  
Field Calibration ◽  
Calibration Program

When the Micro Inertial Measurement Unit is been placed randomly in the case of stationary, the sum vectors that measured by the inertial devices configured orthogonally along three axis, are constant vectors. In view of the above objective facts, a field calibration method of micro inertial measurement unit was proposed. On the base of the establishment and optimization of calibration model, all parameters to be calibrated can be obtained through the least square by the ellipsoid fitting, with the result of high-precision field calibration for micro inertial measurement unit. Finally, a filed calibration program for micro inertial measurement unit is scheduled reasonably. The experiment results show that the method has such characteristics such as easily-operation, time-saving, higher calibration accuracy, and not depending on the baseline direction and datum offered by precision instruments. Especially, it fits for inertial measurement systems which work short time and ask for high accuracy. In addition, it can also significantly increase the measurement accuracy of micro inertial measurement system in practical application.

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.

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