scholarly journals Alternative Inverse Perspective Mapping Homography Matrix Computation for ADAS Systems Using Camera Intrinsic and Extrinsic Calibration Parameters

2021 ◽  
Vol 190 ◽  
pp. 695-700
Author(s):  
Andrejs Rudzitis ◽  
Margarita A. Zaeva
Keyword(s):  
Matrix Computation ◽  
Extrinsic Calibration ◽  
Inverse Perspective Mapping ◽  
Calibration Parameters

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. <br><br> 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. <br><br> 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. <br><br> 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.

Point Laser Triangulation Probe Calibration for Coordinate Metrology

1999 ◽  
Vol 122 (3) ◽  
pp. 582-586 ◽  
Author(s):  
Kevin B. Smith ◽  
Yuan F. Zheng
Keyword(s):  
Contact Force ◽  
Calibration Method ◽  
New Method ◽  
Laser Triangulation ◽  
Coordinate Measuring Machines ◽  
Extrinsic Calibration ◽  
Coordinate Metrology ◽  
Probe Calibration ◽  
Calibration Parameters

Point Laser Triangulation (PLT) probes have significant advantages over traditional touch probes. These advantages include throughput and no contact force, which motivate use of PLT probes on Coordinate Measuring Machines (CMMs). This document addresses the problem of extrinsic calibration. We present a precise technique for calibrating a PLT probe to a CMM. This new method uses known information from a localized polyhedron and measurements taken on the polyhedron by the PLT probe to determine the calibration parameters. With increasing interest in applying PLT probes for point measurements in coordinate metrology, such a calibration method is needed. [S1087-1357(00)01703-2]

The UMA-VI dataset: Visual–inertial odometry in low-textured and dynamic illumination environments

2020 ◽  
Vol 39 (9) ◽  
pp. 1052-1060
Author(s):  
David Zuñiga-Noël ◽  
Alberto Jaenal ◽  
Ruben Gomez-Ojeda ◽  
Javier Gonzalez-Jimenez
Keyword(s):  
Distinctive Feature ◽  
Inertial Measurement Unit ◽  
State Of The Art ◽  
Ground Truth ◽  
Measurement Unit ◽  
Extrinsic Calibration ◽  
Inertial Measurement ◽  
Localization And Mapping ◽  
Calibration Parameters ◽  
Indoor And Outdoor

This article presents a visual–inertial dataset gathered in indoor and outdoor scenarios with a handheld custom sensor rig, for over 80 min in total. The dataset contains hardware-synchronized data from a commercial stereo camera (Bumblebee®2), a custom stereo rig, and an inertial measurement unit. The most distinctive feature of this dataset is the strong presence of low-textured environments and scenes with dynamic illumination, which are recurrent corner cases of visual odometry and simultaneous localization and mapping (SLAM) methods. The dataset comprises 32 sequences and is provided with ground-truth poses at the beginning and the end of each of the sequences, thus allowing the accumulated drift to be measured in each case. We provide a trial evaluation of five existing state-of-the-art visual and visual–inertial methods on a subset of the dataset. We also make available open-source tools for evaluation purposes, as well as the intrinsic and extrinsic calibration parameters of all sensors in the rig. The dataset is available for download at http://mapir.uma.es/work/uma-visual-inertial-dataset

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. <br><br> 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. <br><br> 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. <br><br> 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.

scholarly journals Extrinsic calibration of stereo camera and three-dimensional laser scanner

2019 ◽  
Vol 43 (2) ◽  
pp. 220-230
Author(s):  
A.A. Abramenko
Keyword(s):  
Three Dimensional ◽  
Laser Scanner ◽  
Optimization Methods ◽  
Stereo Pair ◽  
Coordinate Systems ◽  
Extrinsic Calibration ◽  
Stereo Camera ◽  
Point Density ◽  
Calibration Accuracy ◽  
Calibration Parameters

The paper describes an approach that allows solving the problem of extrinsic calibration of a multi-beam lidar and a stereo camera. The approach does not impose any restrictions on the place in which calibration should be performed. Calibration is performed using a calibration board, which is a flat rectangle with special markers. Three-dimensional correspondences are used for calibration. First, a search for the three-dimensional coordinates of the corner points of the calibration board in the coordinate systems of the stereo pair cameras as well as in the coordinate system of the lidar is made. Next, using the optimization methods, calibration parameters are calculated. The results of a series of virtual and real experiments show that the algorithm allows the calibration to be performed with an accuracy comparable to that of sensors. The proposed approach allows one to improve the calibration accuracy due to the simultaneous use of information from two cameras of the stereo pair and is suitable for lidars with both the low and high point density.

scholarly journals Extrinsic Camera Calibration with Line-Laser Projection

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1091
Author(s):  
Izaak Van Crombrugge ◽  
Rudi Penne ◽  
Steve Vanlanduit
Keyword(s):  
Camera Calibration ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Bundle Adjustment ◽  
Field Of View ◽  
Extrinsic Calibration ◽  
Practical Procedure ◽  
Partial Overlap

Knowledge of precise camera poses is vital for multi-camera setups. Camera intrinsics can be obtained for each camera separately in lab conditions. For fixed multi-camera setups, the extrinsic calibration can only be done in situ. Usually, some markers are used, like checkerboards, requiring some level of overlap between cameras. In this work, we propose a method for cases with little or no overlap. Laser lines are projected on a plane (e.g., floor or wall) using a laser line projector. The pose of the plane and cameras is then optimized using bundle adjustment to match the lines seen by the cameras. To find the extrinsic calibration, only a partial overlap between the laser lines and the field of view of the cameras is needed. Real-world experiments were conducted both with and without overlapping fields of view, resulting in rotation errors below 0.5°. We show that the accuracy is comparable to other state-of-the-art methods while offering a more practical procedure. The method can also be used in large-scale applications and can be fully automated.

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