A Novel Method for Automatic Extrinsic Parameter Calibration of RGB-D Cameras

Calibration of extrinsic parameters of the RGB-D camera can be applied in many fields, such as 3D scene reconstruction, robotics, and target detection. Many calibration methods employ a specific calibration object (i.e., a chessboard, cuboid, etc.) to calibrate the extrinsic parameters of the RGB-D color camera without using the depth map. As a result, it is difficult to simplify the calibration process, and the color sensor gets calibrated instead of the depth sensor. To this end, we propose a method that employs the depth map to perform extrinsic calibration automatically. In detail, the depth map is first transformed to a 3D point cloud in the camera coordinate system, and then the planes in the 3D point cloud are automatically detected using the Maximum Likelihood Estimation Sample Consensus (MLESAC) method. After that, according to the constraint relationship between the ground plane and the world coordinate system, all planes are traversed and screened until the ground plane is obtained. Finally, the extrinsic parameters are calculated using the spatial relationship between the ground plane and the camera coordinate system. The results show that the mean roll angle error of extrinsic parameter calibration was −1.14°. The mean pitch angle error was 4.57°, and the mean camera height error was 3.96 cm. The proposed method can accurately and automatically estimate the extrinsic parameters of a camera. Furthermore, after parallel optimization, it can achieve real-time performance for automatically estimating a robot’s attitude.

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Extrinsic Parameter Calibration Method for a Visual/Inertial Integrated System with a Predefined Mechanical Interface

Sensors ◽

10.3390/s19143086 ◽

2019 ◽

Vol 19 (14) ◽

pp. 3086

Author(s):

Ouyang ◽

Shi ◽

You ◽

Zhao

Keyword(s):

Euler Angle ◽

Calibration Method ◽

Integrated System ◽

Corner Detection ◽

Position Vector ◽

Parameter Calibration ◽

Body Frame ◽

Extrinsic Parameter ◽

Extrinsic Parameters ◽

Mechanical Interface

For a visual/inertial integrated system, the calibration of extrinsic parameters plays a crucial role in ensuring accurate navigation and measurement. In this work, a novel extrinsic parameter calibration method is developed based on the geometrical constraints in the object space and is implemented by manual swing. The camera and IMU frames are aligned to the system body frame, which is predefined by the mechanical interface. With a swinging motion, the fixed checkerboard provides constraints for calibrating the extrinsic parameters of the camera, whereas angular velocity and acceleration provides constraints for calibrating the extrinsic parameters of the IMU. We exploit the complementary nature of both the camera and IMU, of which the latter assists in the checkerboard corner detection and correction while the former suppresses the effects of IMU drift. The results of the calibration experiment reveal that the extrinsic parameter accuracy reaches 0.04° for each Euler angle and 0.15 mm for each position vector component (1σ).

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AUTOMATED DETECTION AND CLOSING OF HOLES IN AERIAL POINT CLOUDS USING AN UAS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w6-101-2017 ◽

2017 ◽

Vol XLII-2/W6 ◽

pp. 101-107

Author(s):

T. Fiolka ◽

F. Rouatbi ◽

D. Bender

Keyword(s):

Point Cloud ◽

Ground Plane ◽

Point Clouds ◽

Current Approach ◽

Automated Detection ◽

Lidar System ◽

3D Point Clouds ◽

Terrain Models ◽

3D Terrain ◽

Flight Route

3D terrain models are an important instrument in areas like geology, agriculture and reconnaissance. Using an automated UAS with a line-based LiDAR can create terrain models fast and easily even from large areas. But the resulting point cloud may contain holes and therefore be incomplete. This might happen due to occlusions, a missed flight route due to wind or simply as a result of changes in the ground height which would alter the swath of the LiDAR system. This paper proposes a method to detect holes in 3D point clouds generated during the flight and adjust the course in order to close them. First, a grid-based search for holes in the horizontal ground plane is performed. Then a check for vertical holes mainly created by buildings walls is done. Due to occlusions and steep LiDAR angles, closing the vertical gaps may be difficult or even impossible. Therefore, the current approach deals with holes in the ground plane and only marks the vertical holes in such a way that the operator can decide on further actions regarding them. The aim is to efficiently create point clouds which can be used for the generation of complete 3D terrain models.

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A New Kinect-Based Scanning System and its Application

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.764-765.1375 ◽

2015 ◽

Vol 764-765 ◽

pp. 1375-1379 ◽

Cited By ~ 1

Author(s):

Cheng Tiao Hsieh

Keyword(s):

Point Cloud ◽

Depth Map ◽

Bilateral Filter ◽

Point Clouds ◽

Digital Fabrication ◽

Surface Model ◽

Scanning System ◽

3D Registration ◽

Noise Point

This paper aims at presenting a simple approach utilizing a Kinect-based scanner to create models available for 3D printing or other digital manufacturing machines. The outputs of Kinect-based scanners are a depth map and they usually need complicated computational processes to prepare them ready for a digital fabrication. The necessary processes include noise filtering, point cloud alignment and surface reconstruction. Each process may require several functions and algorithms to accomplish these specific tasks. For instance, the Iterative Closest Point (ICP) is frequently used in a 3D registration and the bilateral filter is often used in a noise point filtering process. This paper attempts to develop a simple Kinect-based scanner and its specific modeling approach without involving the above complicated processes.The developed scanner consists of an ASUS’s Xtion Pro and rotation table. A set of organized point cloud can be generated by the scanner. Those organized point clouds can be aligned precisely by a simple transformation matrix instead of the ICP. The surface quality of raw point clouds captured by Kinect are usually rough. For this drawback, this paper introduces a solution to obtain a smooth surface model. Inaddition, those processes have been efficiently developed by free open libraries, VTK, Point Cloud Library and OpenNI.

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An Effective Randomized Hough Transform Method to Extract Ground Plane from Kinect Point Cloud

2019 International Conference on Information and Communication Technology Convergence (ICTC) ◽

10.1109/ictc46691.2019.8939968 ◽

2019 ◽

Author(s):

Hoa Dang Khanh ◽

Van Son Nguyen ◽

Anh Do ◽

Nguyen Tien Dzung

Keyword(s):

Hough Transform ◽

Point Cloud ◽

Ground Plane ◽

Transform Method ◽

Randomized Hough Transform

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

Sensors ◽

10.3390/s20102841 ◽

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.

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Retrieving individual tree heights from a point cloud generated with optical imagery from an unmanned aerial vehicle (UAV)

Canadian Journal of Forest Research ◽

10.1139/cjfr-2019-0418 ◽

2020 ◽

Vol 50 (10) ◽

pp. 1012-1024

Author(s):

Meimei Wang ◽

Jiayuan Lin

Keyword(s):

Unmanned Aerial Vehicle ◽

Point Cloud ◽

Laser Scanning ◽

Tree Height ◽

Absolute Error ◽

Coefficient Of Determination ◽

Surface Model ◽

Individual Tree ◽

Aerial Vehicle ◽

The Mean

Individual tree height (ITH) is one of the most important vertical structure parameters of a forest. Field measurement and laser scanning are very expensive for large forests. In this paper, we propose a cost-effective method to acquire ITHs in a forest using the optical overlapping images captured by an unmanned aerial vehicle (UAV). The data sets, including a point cloud, a digital surface model (DSM), and a digital orthorectified map (DOM), were produced from the UAV imagery. The canopy height model (CHM) was obtained by subtracting the digital elevation model (DEM) from the DSM removed of low vegetation. Object-based image analysis was used to extract individual tree crowns (ITCs) from the DOM, and ITHs were initially extracted by overlaying ITC outlines on the CHM. As the extracted ITHs were generally slightly shorter than the measured ITHs, a linear relationship was established between them. The final ITHs of the test site were retrieved by inputting extracted ITHs into the linear regression model. As a result, the coefficient of determination (R2), the root mean square error (RMSE), the mean absolute error (MAE), and the mean relative error (MRE) of the retrieved ITHs against the measured ITHs were 0.92, 1.08 m, 0.76 m, and 0.08, respectively.

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Research on Pose Estimation for Electric Vehicle’s Fast-Swap Battery Box Based on Visual Information

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.253-255.2252 ◽

2012 ◽

Vol 253-255 ◽

pp. 2252-2257

Author(s):

Yu Ming Wu ◽

Shuo Liu ◽

Gao Yang Zhang ◽

Xiao Yan Yin ◽

Ming Yu Zhao ◽

...

Keyword(s):

Coordinate System ◽

Pose Estimation ◽

Visual Information ◽

Experimental Results ◽

Noise Effects ◽

Simulation Results ◽

Camera Coordinate System

For the reason of difficult to get battery box pose information, we research the battery box pose measure method based on visual information. We get the coplanar four points at the lines constraints which extracted form image. We get the pose relationship between the battery box coordinate system and camera coordinate system, and then calculate the average of the measure results to reduce noise effects for measure precision. Simulation results show that the method through calculate average of the measure results can effectively reduce noise effects for measure accuracy. The actual experimental results show that the pose estimate accuracy is meet robot requirements for battery swap.

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Reduced conformal geometrodynamics

International Journal of Modern Physics A ◽

10.1142/s0217751x20400230 ◽

2020 ◽

Vol 35 (02n03) ◽

pp. 2040023 ◽

Cited By ~ 1

Author(s):

Andrej B. Arbuzov ◽

Alexander E. Pavlov

Keyword(s):

Gravitational Field ◽

Dynamical Systems ◽

Coordinate System ◽

Tangent Space ◽

Equations Of Motion ◽

Mean Value ◽

Hamiltonian Reduction ◽

Square Root ◽

The Mean ◽

Static Background

The global time in geometrodynamics is defined in a covariant under diffeomorphisms form. An arbitrary static background metric is taken in the tangent space. The global intrinsic time is identified with the mean value of the logarithm of the square root of the ratio of the metric determinants. The procedures of the Hamiltonian reduction and deparametrization of dynamical systems are implemented. The reduced Hamiltonian equations of motion of gravitational field in semi-geodesic coordinate system are written.

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