Calibration of a 2D laser scanner system and rotating platform using a point-plane constraint

Kurnianggoro, Laksono; Hoang, Van-Dung; Jo, Kang-Hyun

doi:10.2298/csis141020093k

Cited by 28 publications

(22 citation statements)

References 24 publications

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“…11. With this, it is conclusive that by combining both the azimuth and elevation axes, a 3D scanning mechanism could be realized using a 2D LiDAR sensor [9,13].…”

Section: D Scanning Testmentioning

confidence: 76%

3D Indoor Mapping System Using 2D LiDAR Sensor for Drones

Shahrin

Hashim²,

Zaki³

et al. 2018

IJET

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Most 3D scanners are heavy, bulky and costly. These are the major factors that make them irrelevant to be attached to a drone for autonomous navigation. With modern technologies, it is possible to design a simple 3D scanner for autonomous navigation. The objective of this study is to design a cost effective 3D indoor mapping system using a 2D light detection and ranging (LiDAR) sensor for a drone. This simple 3D scanner is realised using a LiDAR sensor together with two servo motors to create the azimuth and elevation axes. An Arduino Uno is used as the interface between the scanner and computer for the real-time communication via serial port. In addition, an open source Point-Cloud Tool software is used to test and view the 3D scanner data. To study the accuracy and efficiency of the system, the LiDAR sensor data from the scanner is obtained in real-time in point-cloud form. The experimental results proved that the proposed system can perform the 2D and 3D scans with tolerable performance.

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“…11. With this, it is conclusive that by combining both the azimuth and elevation axes, a 3D scanning mechanism could be realized using a 2D LiDAR sensor [9,13].…”

Section: D Scanning Testmentioning

confidence: 76%

3D Indoor Mapping System Using 2D LiDAR Sensor for Drones

Shahrin

Hashim²,

Zaki³

et al. 2018

IJET

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“…Proposed algorithms in both [7] [6] papers, an intensity image is used in conjunction with a checkerboard pattern. Methodology proposed in [17] finds the rotation axis and the rotation radius by solving a linear equation constructed from a point-plane constraint established by scanning a checker-board calibration pattern in several poses. Using the decoupling property of which a line on a plane does not contain 3DoF translation terms, a method was proposed in [18] to find the six parameters between the two centres of LRF and a rotating platform.…”

Section: Related Workmentioning

confidence: 99%

Calibration of a Rotating Laser Range Finder using Intensity Features

Katuwandeniya

Ranasinghe

Dantanarayana

et al. 2018

2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)

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This paper presents an algorithm for calibrating a "3D range sensor" constructed using a two-dimensional laser range finder (LRF), that is rotated about an axis using a motor to obtain a three-dimensional point cloud. The sensor assembly is modelled as a two degree of freedom open kinematic chain, with one joint corresponding to the axis of the internal mirror in the LRF and the other joint set along the axis of the motor used to rotate the body of the LRF. In the application described in this paper, the sensor unit is mounted on a robot arm used for infrastructure inspection. The objective of the calibration process is to obtain the coordinate transform required to compute the locations of the 3D points with respect to the robot coordinate frame. Proposed strategy uses observations of a set of markers arbitrarily placed in the environment. Distances between these markers are measured and a metric multidimensional scaling is used to obtain the coordinates of the markers with respect to a local coordinate frame. Intensity associated with each beam point of a laser scan is used to locate the reflective markers in the 3D point cloud and a least squares problem is formulated to compute the relationship between the robot coordinate frame, LRF coordinate frame and the marker coordinate frame. Results from experiments using the robot, LRF combination to map a cavity inside a steel bridge structure are presented to demonstrate the effectiveness of the calibration process.

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“…where [u, v] ⊤ denotes image coordinates in the coordinate system (o The position and orientation information provided by the LRF sensor is also calibrated via a special method (Kurnianggoro et al, 2014). With the calibrated LRF, the local bridge section is scanned and the corresponding 3D model is derived from the scanning data.…”

Section: Calibrationsmentioning

confidence: 99%

Optimal Image Stitching for Concrete Bridge Bottom Surfaces Aided by 3d Structure Lines

Liu¹,

Yao²,

Liu³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Crack detection for bridge bottom surfaces via remote sensing techniques is undergoing a revolution in the last few years. For such applications, a large amount of images, acquired with high-resolution industrial cameras close to the bottom surfaces with some mobile platform, are required to be stitched into a wide-view single composite image. The conventional idea of stitching a panorama with the affine model or the homographic model always suffers a series of serious problems due to poor texture and out-of-focus blurring introduced by depth of field. In this paper, we present a novel method to seamlessly stitch these images aided by 3D structure lines of bridge bottom surfaces, which are extracted from 3D camera data. First, we propose to initially align each image in geometry based on its rough position and orientation acquired with both a laser range finder (LRF) and a high-precision incremental encoder, and these images are divided into several groups with the rough position and orientation data. Secondly, the 3D structure lines of bridge bottom surfaces are extracted from the 3D cloud points acquired with 3D cameras, which impose additional strong constraints on geometrical alignment of structure lines in adjacent images to perform a position and orientation optimization in each group to increase the local consistency. Thirdly, a homographic refinement between groups is applied to increase the global consistency. Finally, we apply a multi-band blending algorithm to generate a large-view single composite image as seamlessly as possible, which greatly eliminates both the luminance differences and the color deviations between images and further conceals image parallax. Experimental results on a set of representative images acquired from real bridge bottom surfaces illustrate the superiority of our proposed approaches.

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Calibration of a 2D laser scanner system and rotating platform using a point-plane constraint

Cited by 28 publications

References 24 publications

3D Indoor Mapping System Using 2D LiDAR Sensor for Drones

3D Indoor Mapping System Using 2D LiDAR Sensor for Drones

Calibration of a Rotating Laser Range Finder using Intensity Features

Optimal Image Stitching for Concrete Bridge Bottom Surfaces Aided by 3d Structure Lines

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