Calibration Of External Parameters Research Articles

农业环境高精度三维点云地图构建方法研究

In agricultural operation scenarios, the diversity of farmland terrain, crops and other forms, as well as uncertain factors such as weather changes and crop growth during agricultural operation, can have an impact on the construction of high-precision maps. To address these challenges and analyze operational scenarios based on the characteristics of agricultural scenarios, this paper proposes a point cloud map construction algorithm for plant point removal and locatability estimation. Based on the existing Simultaneous Localization and Mapping (SLAM) framework, plant point removal and locatability estimation are improved. Firstly, Red, Green, Blue (RGB) images and Near Infrared (NIR) images are fused to identify and remove plant point clouds, preserving effective inter frame matching information, reducing the impact of dynamic points on inter frame matching, and achieving high front-end motion estimation accuracy. Then, the localization estimation method based on learning is used to determine the motion estimation status and determine whether to execute the backend optimization algorithm. Finally, the back-end optimization algorithm based on Factor graph is designed, and the Factor graph, constraint relationship and optimization function are constructed to optimize the pose of all frames. The optimized map construction algorithm reduces the re projection errors between field roads, paths, and crop rows by 10.27%, 20.76%, and 14.36% compared to before optimization. To verify the actual operational effectiveness of the point cloud map construction algorithm, the hardware part of the multi-sensor information collection system was designed, and sensor internal and external parameter calibration were also carried out. A map information collection vehicle was built and field experiments were conducted. The results showed that the positioning error of the point cloud map construction method proposed in this paper is less than 0.5°, and the cumulative error of 30 m translation is less than 12 cm, which meets the actual operational requirements.

The application of deep learning in autonomous driving

Autonomous driving technology is currently a globally prominent subject, with its relevance and impact readily apparent. Leveraging advanced sensors, sophisticated algorithms, and state-of-the-art computer vision techniques, autonomous vehicles can autonomously navigate, mitigate traffic accidents, and alleviate urban congestion. Furthermore, this technology is poised to accelerate innovation within the automotive sector, drive industrial advancement, and enhance people's convenience and safety in their travel experiences. The importance of autonomous driving technology is not only reflected in its own advantages, but also in its ability to lead the development direction of intelligent transportation in the future, helping to promote the development of intelligent transportation systems, realize the intelligent interconnection between vehicles and vehicles, vehicles and road infrastructure, and further enhance the safety and convenience of traffic travel. Therefore, autonomous driving technology is a significant innovation that will bring a better and more convenient future for mankind. In this study, a method based on sensor internal and external parameter calibration transformation matrix, road target detection algorithm, automatic vehicle detection method, pedestrian intention prediction technology and automatic pedestrian recognition system are analyzed, which are applied to object detection and obstacle avoidance. This article provides a good overview of the field of autonomous driving.

Multi-Lidar System Localization and Mapping with Online Calibration

Currently, the demand for automobiles is increasing, and daily travel is increasingly reliant on cars. However, accompanying this trend are escalating traffic safety issues. Surveys indicate that most traffic accidents stem from driver errors, both intentional and unintentional. Consequently, within the framework of vehicular intelligence, intelligent driving uses computer software to assist drivers, thereby reducing the likelihood of road safety incidents and traffic accidents. Lidar, an essential facet of perception technology, plays an important role in vehicle intelligent driving. In real-world driving scenarios, the detection range of a single laser radar is limited. Multiple laser radars can improve the detection range and point density, effectively mitigating state estimation degradation in unstructured environments. This, in turn, enhances the precision and accuracy of synchronous positioning and mapping. Nonetheless, the relationship governing pose transformation between multiple lidars is intricate. Over extended periods, perturbations arising from vibrations, temperature fluctuations, or collisions can compromise the initially converged external parameters. In view of these concerns, this paper introduces a system capable of concurrent multi-lidar positioning and mapping, as well as real-time online external parameter calibration. The method first preprocesses the original measurement data, extracts linear and planar features, and rectifies motion distortion. Subsequently, leveraging degradation factors, the convergence of the multi-lidar external parameters is detected in real time. When deterioration in external parameters is identified, the local map of the main laser radar and the feature point cloud of the auxiliary laser radar are associated to realize online calibration. This is succeeded by frame-to-frame matching according to the converged external parameters, culminating in laser odometer computation. Introducing ground constraints and loop closure detection constraints in the back-end optimization effectuates global estimated pose rectification. Concurrently, the feature point cloud is aligned with the global map, and map update is completed. Finally, experimental validation is conducted on data acquired from Chang’an University to substantiate the system’s online calibration and positioning mapping accuracy, robustness, and real-time performance.

High-Precision External Parameter Calibration Method for Camera and Lidar Based on a Calibration Device

The fusion of camera and lidar plays an important role in the field of robotic perception. The accurate external parameter calibration is a necessary prerequisite for sensor fusion. Herein, an auxiliary calibration device with distinctive geometric features was designed to address the problems of low accuracy and poor robustness associated with external parameter calibrations of camera and lidar. Moreover, a coarse-to-fine two-stage calibration method was proposed for the external parameters of the camera and lidar. The first stage of the method is the extraction of multiple groups of two-dimensional (2D) and three-dimensional (3D) lines corresponding to the edge of the calibration device from the image and lidar point cloud that yields a unique initial estimation of the external parameters. In the second stage, the 2D–3D center point of the sphere of the calibration device was detected, and the initial external parameters were further optimized using a nonlinear optimization method. The proposed method provides two different features that add stability against noise to the calibration system. Both simulated and actual experiments show that the method can yield high-precision external parameters without an initial value. Compared to state-of-the-art methods, our method has advantages in terms of accuracy. the calibration system has a certain degree of noise resistance and stability under different laser noise and vertical resolution.

A spatiotemporal registration method between roadside lidar and camera

With the rapid development of intelligent transportation, it is of great significance to improve road traffic safety and traffic efficiency by tracking road users in real time. In this paper, a roadside multi-source data acquisition platform is designed based on two main sensors, lidar and camera, to perform time synchronization between lidar data and camera data. The point cloud data and image data of the same frame are the research objects. Based on Zhang Zhengyou’s calibration method and the plane target model theory, the camera internal parameters and the joint external parameters of the lidar and camera are respectively calibrated to obtain the internal and external parameter matrix. The error is 0.09 pixels, and the external parameter calibration error is 0.22 pixels. The registration test of lidar and camera under different roadside layout conditions is designed to verify the adaptability of the registration method. When the camera is arranged and installed, the distance between the horizontal and vertical directions should not exceed 90 cm.

Multivision Sensor Extrinsic Calibration Method With Non-Overlapping Fields of View Using Encoded 1D Target

Multiple vision sensors (multi-VSs) are difficult to calibrate in large fields of view vision measurement owing to the lack of overlapping areas. Thus, this study proposes a novel method for the external parameter calibration of multi-VSs by using an encoded 1D target. The quick response code is used in this study to uniquely identify the feature circle. The Hough transform method and ellipse center solving method are jointly applied to optimize the feature points and further improve the collinearity of the feature points. As the cross-ratio of the collinearity feature points involves a projection invariance, the rotation and translation vectors between the multi-VSs are solved by the inverse process of projection and rigid transformation. The accuracy and advantages of the proposed method are verified by simulation and real experiments. The proposed target has a simple structure, the operation is convenient and flexible, and the method can be applied to the global calibration of multi-VSs with non-overlapping fields of view.

Underwater Camera Calibration Method Based on Improved Slime Mold Algorithm

The maritime transportation line is the lifeblood of the national economy. Transportation facilities and their construction equipment need to obtain the environmental parameters of relevant sea areas, and underwater robots are the first type of auxiliary equipment for underwater road construction. Considering that the construction of underwater transportation facilities puts forward higher requirements for the observation accuracy of underwater robots, the reliability of internal and external parameter calibration of underwater cameras directly affects the accuracy of underwater positioning and measurement. In order to improve the calibration accuracy of underwater cameras, this paper establishes a real underwater camera calibration image data set, integrates the optimal neighborhood disturbance and reverse learning strategy on the basis of the slime mold optimization algorithm, optimizes the calibration results of Zhang’s traditional calibration method, and compares the optimization results and reprojection error of the ORSMA algorithm with Zhang’s calibration method the SMA algorithm, SOA algorithm and PSO algorithm to verify the accuracy and effectiveness of the proposed algorithm.

High Precision Calibration Algorithm for Binocular Stereo Vision Camera using Deep Reinforcement Learning.

Camera calibration is the most important aspect of computer vision research. To address the issue of insufficient precision, therefore, a high precision calibration algorithm for binocular stereo vision camera using deep reinforcement learning is proposed. Firstly, a binocular stereo camera model is established. Camera calibration is mainly divided into internal and external parameter calibration. Secondly, the internal parameter calibration is completed by solving the antihidden point of the camera light center and the camera distortion value of the camera plane. The deep learning fitting value function is used based on the internal parameters. The target network is established to adjust the parameters of the value function, and the convergence of the value function is calculated to optimize reinforcement learning. The deep reinforcement learning fitting structure is built, the camera data is entered, and the external parameter calibration is finished by continuous updating and convergence. Finally, the high precision calibration of the binocular stereo vision camera is completed. The results show that the calibration error of the proposed algorithm under different sizes of checkerboard calibration board test is only 0.36% and 0.35%, respectively, the calibration accuracy is high, the value function converges quickly, and the parameter calculation accuracy is high, the overall time consumption of the proposed algorithm is short, and the calibration results have strong stability.

Rough Registration of BIM Element Projection for Construction Progress Tracking

Monitoring construction progress is essential for project management. A variety of excellent automatic schedule acquisition methods have been proposed, particularly 3D reconstruction. However, both laser-based and image-based 3D reconstruction methods rely on point clouds which have some inherent defects. In this study, a rough registration method was proposed to obtain construction progress. The method does not generate point clouds during the entire process but determines the actual construction progress by registering the target detection results and the projection of building information model elements. The method was tested at the construction sites of commercial and residential buildings. The experimental results indicated that the registration accuracy reached <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">95.13%</b> . The average external parameter calibration time and the registration time of each image are <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">18.57s</b> and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50.59ms</b> , respectively. Compared to similar 3D reconstruction methods, the proposed method is realistic, fast, and simple. This provides a promising method for 3D reconstruction from unordered construction-site images.

A progressive online external parameter calibration and initialization method for stereo-IMU system

PurposeThis paper aims to present a pipeline to progressively deal with the online external parameter calibration and estimator initialization of the Stereo-inertial measurement unit (IMU) system, which does not require any prior information and is suitable for system initialization in a variety of environments.Design/methodology/approachBefore calibration and initialization, a modified stereo tracking method is adopted to obtain a motion pose, which provides prerequisites for the next three steps. Firstly, the authors align the pose obtained with the IMU measurements and linearly calculate the rough external parameters and gravity vector to provide initial values for the next optimization. Secondly, the authors fix the pose obtained by the vision and restore the external and inertial parameters of the system by optimizing the pre-integration of the IMU. Thirdly, the result of the previous step is used to perform visual-inertial joint optimization to further refine the external and inertial parameters.FindingsThe results of public data set experiments and actual experiments show that this method has better accuracy and robustness compared with the state of-the-art.Originality/valueThis method improves the accuracy of external parameters calibration and initialization and prevents the system from falling into a local minimum. Different from the traditional method of solving inertial navigation parameters separately, in this paper, all inertial navigation parameters are solved at one time, and the results of the previous step are used as the seed for the next optimization, and gradually solve the external inertial navigation parameters from coarse to fine, which avoids falling into a local minimum, reduces the number of iterations during optimization and improves the efficiency of the system.

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

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;In the field of automatic driving, the combination of 3D LIDAR and inertial measurement unit (IMU) is a common sensor configuration scheme in laser point-cloud localization, high-precision map making and point-cloud target detection. So it is critical to calibrate LIDAR and IMU accurately. At present, due to the large volume and high cost of 3D LIDAR with high-line-number(Such as 64 lines or 128 lines), the configuration scheme of using multiple low-line-number 3D LIDARs appears in the automatic driving vehicle sensing system. However, the common calibration methods are not suitable for multi 3D LIDARs and IMU parameters calibration on autonomous vehicle, which have the disadvantages of cumbersome implementation and low accuracy. In this paper, a joint calibration test platform composed of dual LIDARs and IMU is assembled, and a method of precise automatic calibration based on GPS/RTK data is proposed. Firstly, the initial parameters of the main 3D LIDAR and IMU are obtained by hand-eye calibration method, and then the motion distortion of the point cloud are removed by using the pose information. After global and local optimization of nearest neighbor error, the conversion parameters from the main LIDAR to IMU are obtained. Then, the remaining LIDARs are calibrated with the main LIDAR by combining coarse registration and fine registration, and finally realize the automatic calibration of external parameters of the entire system. The experimental results show that the proposed method has high calibration accuracy for the system composed of multiple 3D LIDARs and IMU, and the calibration effect is stable.&lt;/div&gt;&lt;/div&gt;

A novel algorithm based on nonlinear optimization for parameters calibration of wheeled robot mobile chasses

Mobile robot positioning, mapping, and navigation systems generally employ an inertial measurement unit to obtain the acceleration and angular velocity of the robot. However, errors in the internal and external parameters of an inertial measurement unit arising from defective calibration directly affect the accuracy of robot positioning and pose estimation. While this issue has been addressed by the mature internal parameters calibration method available for inertial measurement unit, external reference calibration method between the inertial measurement unit and the chassis of a mobile robot are lacking. This study addresses this issue by proposing a novel algorithm for internal parameter calibration of mecanum wheel omnidirectional mobile platform and external parameter calibration of mecanum chassis- inertial measurement unit based on principal component analysis and nonlinear optimization, which is designed for robots equipped with cameras, inertial measurement unit, mecanum wheels, and wheel speed odometers, and functions under the premise of accurate calibrations for the internal parameters of the inertial measurement unit and the internal and external parameters of the camera. All of the internal and external parameters calibrations are conducted using the robot's existing equipment without the need for additional calibration aids. The feasibility of the method is verified by its application to a mecanum wheel omnidirectional mobile platform. The proposed calibration method is thereby demonstrated to guarantee the accuracy of robot pose estimation.

Calibration of external parameters of binocular cloud height system based on odometer method

双目成像系统测量云底高基线长，外参标定困难。提出一种适用于地基云高测量系统的双目相机外参标定方法，采用ORB算法提取与匹配左右云图同名点，然后根据对极几何约束计算两个相机之间的旋转平移关系。为验证该算法的精度，在室外搭建基线60 m的双目云高系统，采用里程计法标定外参，标定得到两个相机之间的距离误差为34.44 cm，精度达到99.43%。通过分析校正后的双目云图和天空视差图，验证了大基线里程计外参标定法的精度较高。

Calibration of Large-Scale Spatial Positioning Systems Based on Photoelectric Scanning Angle Measurements and Spatial Resection in Conjunction with an External Receiver Array

Positioning systems providing high-precision real-time measurements over very large spatial scales are urgently required for large-scale industrial manufacturing applications. While large-scale positioning systems (LSPSs) employing laser transmitter stations have been employed in engineering practice, the introduction of an LSPS into an existing industrial manufacturing setting must first solve the problems of docking with existing control points and external parameter calibration. However, calibrating the external parameters of a measurement system is very difficult under extreme and complicated working conditions due to the limited visibility of transmitter stations and the measurement distances involved. This problem is addressed in this paper by proposing a single transmitter station calibration method based on a photoelectric scanning multi-angle resection positioning model that combines photoelectric scanning angle measurements and spatial resection in conjunction with an external receiver array. Positioning information is obtained by solving the unknown parameters of the model according to a nonlinear optimization approach using the Levenberg–Marquardt least-squares fitting algorithm. The feasibility and spatial positioning accuracy of the proposed method are verified experimentally. The experimental results demonstrate that the principles of the proposed method are correct, and the method can achieve millimeter measurement accuracy, which meets the requirements of measurement tasks in engineering applications.

An External Parameter Optimize Method for 3D Optical Measurement System

A simple and effective external parameter calibration and optimize method based on epipolar geometry for improving the accuracy and stabilization of 3D optical measurement system are present. First, the internal parameters of two cameras are calibrated using the planar calibration method. Then using the parameters as initial value calibrates the external parameter of the 3D optical measurement system in every view. Optimization result is computed through minimizing epipolar line error of the same pair of points in every view. Experimental results show that the measurement precision is high and stable in the measurement volume.

다중 평면의 3차원 모서리를 이용한 레이저 거리센서 및 카메라의 정밀 보정

레이저 거리센서와 비전 카메라의 정보를 융합하기 위해서는 두 센서 사이의 상대적인 위치관계를 설명하는 외부 파라미터를 정확하게 보정하는 것이 필수적이다. 본 논문에서는 레이저 거리센서와 카메라 좌표계 간의 외부 파라미터를 기존에 알려진 방법보다 쉬우면서도 정확하게 획득할 수 있는 새로운 보정 방법을 제안하고자 한다. 본 논문에서 제안한 방법의 접근법은 레이저 거리센서로 획득한 3차원 구조물의 모서리 정보를 영상으로 투영하였을 때 반드시 하나의 직선상에 존재해야 한다는 것을 제약조건으로 한다. 이러한 제약조건을 만족하는 3차원 기하모델을 제시하고 이 모델의 에너지 함수를 최소화하기 위한 수치적 해법을 소개한다. 또한 높은 정밀도의 보정을 위하여 레이저 거리정보 및 카메라 영상의 획득 과정에 대해서도 상세히 설명한다. 실험을 통해 제안된 방법의 성능이 기존의 방법에 비하여 보다 높은 정밀도를 보임을 확인할 수 있었다. For data fusion of laser range finder (LRF) and vision camera, accurate calibration of external parameters which describe relative pose between two sensors is necessary. This paper proposes a new calibration method which can acquires more accurate external parameters between a LRF and a vision camera compared to other existing methods. The main motivation of the proposed method is that any corner data of a known 3D structure which is acquired by the LRF should be projected on a straight line in the camera image. To satisfy such constraint, we propose a 3D geometric model and a numerical solution to minimize the energy function of the model. In addition, we describe the implementation steps of the data acquisition of LRF and camera images which are necessary in accurate calibration results. In the experiment results, it is shown that the performance of the proposed method are better in terms of accuracy compared to other conventional methods.

A novel method for camera external parameters online calibration using dotted road line

On board camera is the most important information source for driver assistant application which is based on computer vision. One problem for on board camera is that the external parameters are easy to be changed when moving on the road. Traditional method either calibrates external parameters offline or calibrates external parameters semi-online, which both need human intervention. By observation, it is found that corner connection of dotted road lane can form two groups of parallel lines. Using this geometric characteristics and through theoretical derivation, a novel online camera external parameter calibration method is proposed which focus on the situation when vehicle is moving. The pro of this method is that it maintains relatively high calculation accuracy and more important, it does not require any human intervention in whole calibration process. Experimental and comparison results show that this method is simple and have accurate results which fully meet the requirements of practical application.

An external parameter calibration method for multiple cameras based on laser rangefinder

In this paper, an external parameter calibration method for multiple cameras with non-overlapping fields of view (FOVs) using a laser rangefinder (LRF) is presented. The proposed method combines multiple cameras by the laser beam projected by the LRF. The main procedures of the proposed method are: The LRF is positioned for a few (at least two) times and projects a laser beam across the FOVs of different cameras. The laser beam is intercepted by a planar object for at least 3times in the FOV of each camera. The cameras capture the laser spots projected on the planar object and the LRF measures the distances between the different spots. According to the co-linearity of the laser spots captured by all the cameras at each LRF position and the distances between the laser spots, the external parameters between different cameras can be computed. If the laser beam cannot reach the FOVs of all the cameras, the calibration of all the cameras can be achieved by pair-wise calibration. Experiment results show that the relative point pair distance error is around 0.3‰ when the distance between the two cameras is around four meters.

External parameter calibration of widely distributed vision sensors with non-overlapping fields of view

In this paper, an external parameter calibration method for multiple vision sensors (MVS) with non-overlapping fields-of-view (FOV) using spot laser is presented. The main procedures of the method are as follows: Spot laser projects laser beam across the FOVs of multiple vision sensors, the spot laser is placed at least two times. At each spot laser position, the planar target intercepts the laser beam at least two times in the FOV of each vision sensor, and the 3D coordinates of the laser spots projected on the planar target are reconstructed. The coordinate transformation from each vision sensor to global coordinate frame (GCF) is computed according to the co-linearity of the laser spots at each spot laser position. The proposed method connects the non-overlapping MVS by the laser beam projected by spot laser. It does not require any complex equipment, but one spot laser and one planar target. The experiment shows that when the working distance of the cameras is about 500mm–1100mm and field-of-view is around 500mm×400mm, the point pair distance RMS error of the calibrated measurement system is about 1.2mm with respect to 1.1m measurement range.

A Special Unique Solution Case of the Perspective-Three-Point Problem for External Parameter Calibration of an Omnidirectional Camera

In the field of machine vision, the multi-solution problem of the perspective-three-point (P3P) camera pose estimation problem has been widely researched. Many researchers have tried to find unique solutions that will enable the use of the P3P method in practical applications such as the calibration of external parameters of a camera. Gao found a special case that is very simple and intuitive, but can only be used for a camera with a very large field of view. Therefore, it did not attract much attention. The purpose of this study was to analyse Gao's special case and use it for calibrating the external parameters of an omnidirectional camera, which is used widely because of its very large field of view. We first verify Gao's special case and extend its application. We then propose an iterative method for finding the unique solution. After this, we perform a simulation experiment to determine the effect of the configuration of the three control points on the accuracy of the unique solution. Finally, we use an application to examine the efficiency of the method.