A Novel Method for LiDAR Camera Calibration by Plane Fitting

Chai, Ziqi; Sun, Yuxin; Xiong, Zhenhua

doi:10.1109/aim.2018.8452339

Cited by 33 publications

(10 citation statements)

References 4 publications

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“…For the 3D modeling of point cloud data, the extraction of regular shapes (i.e., plane, sphere, and cylinder) from point coordinates is an important step for the usage of the point cloud. For example, many studies have tried to develop plane fitting methods for depth calibration [19,20], object segmentation [9,10], and 3D environment reconstruction [21][22][23]. In general, the plane estimation algorithms of the point cloud can be divided into three groups.…”

Section: Introductionmentioning

confidence: 99%

“…Although they were first introduced for the game industry where the accuracy of the measurements is not crucial, RGB-D sensors have been applied for many high accuracy applications recently, for example, indoor 3D modeling [15], simultaneous localization and mapping (SLAM) [16], and augmented reality applications [17], etc., in which the rigorous calibration and error modeling of RGB-D sensor data become increasingly essential [18].For the 3D modeling of point cloud data, the extraction of regular shapes (i.e., plane, sphere, and cylinder) from point coordinates is an important step for the usage of the point cloud. For example, many studies have tried to develop plane fitting methods for depth calibration [19,20], object segmentation [9,10], and 3D environment reconstruction [21][22][23]. In general, the plane estimation algorithms of the point cloud can be divided into three groups.…”

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confidence: 99%

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Improving Plane Fitting Accuracy with Rigorous Error Models of Structured Light-Based RGB-D Sensors

Darwish

et al. 2020

Remote Sensing

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Plane fitting is a fundamental operation for point cloud data processing. Most existing methods for point cloud plane fitting have been developed based on high-quality Lidar data giving equal weight to the point cloud data. In recent years, using low-quality RGB-Depth (RGB-D) sensors to generate 3D models has attracted much attention. However, with low-quality point cloud data, equal weight plane fitting methods are not optimal as the range errors of RGB-D sensors are distance-related. In this paper, we developed an accurate plane fitting method for a structured light (SL)-based RGB-D sensor. First, we derived an error model of a point cloud dataset from the SL-based RGB-D sensor through error propagation from the raw measurement to the point coordinates. A new cost function based on minimizing the radial distances with the derived rigorous error model was then proposed for the random sample consensus (RANSAC)-based plane fitting method. The experimental results demonstrated that our method is robust and practical for different operating ranges and different working conditions. In the experiments, for the operating ranges from 1.23 meters to 4.31 meters, the mean plane angle errors were about one degree, and the mean plane distance errors were less than six centimeters. When the dataset is of a large-depth-measurement scale, the proposed method can significantly improve the plane fitting accuracy, with a plane angle error of 0.5 degrees and a mean distance error of 4.7 cm, compared to 3.8 degrees and 16.8 cm, respectively, from the conventional un-weighted RANSAC method. The experimental results also demonstrate that the proposed method is applicable for different types of SL-based RGB-D sensor. The rigorous error model of the SL-based RGB-D sensor is essential for many applications such as in outlier detection and data authorization. Meanwhile, the precise plane fitting method developed in our research will benefit algorithms based on high-accuracy plane features such as depth calibration, 3D feature-based simultaneous localization and mapping (SLAM), and the generation of indoor building information models (BIMs).

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Improving Plane Fitting Accuracy with Rigorous Error Models of Structured Light-Based RGB-D Sensors

Darwish

et al. 2020

Remote Sensing

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“…Many extrinsic calibration methods between 360-degree LiDAR and vision camera have been proposed. However, they mostly require a specially designed calibration board or calibration object [4][5][6][7][8]. Zhou et al [4] used a planar calibration board that has three circular holes.…”

Section: Introductionmentioning

confidence: 99%

Extrinsic Calibration between Camera and LiDAR Sensors by Matching Multiple 3D Planes

Kim

Park

2019

Sensors

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Tel.: +82-53-950-7575 † This paper is an extended version of E. S. Kim and S. Y. Park, "Extrinsic calibration of a camera-LIDAR multi sensor system using a planar chessboard," published in the Abstract: This paper proposes a simple extrinsic calibration method for a multi-sensor system which consists of six image cameras and a 16-channel 3D LiDAR sensor using a planar chessboard. The six cameras are mounted on a specially designed hexagonal plate to capture omnidirectional images and the LiDAR sensor is mounted on the top of the plates to capture 3D points in 360 degrees. Considering each camera-LiDAR combination as an independent multi-sensor unit, the rotation and translation between the two sensor coordinates are calibrated. The 2D chessboard corners in the camera image are reprojected into 3D space to fit to a 3D plane with respect to the camera coordinate system. The corresponding 3D point data that scan the chessboard are used to fit to another 3D plane with respect to the LiDAR coordinate system. The rotation matrix is calculated by aligning normal vectors of the corresponding planes. In addition, an arbitrary point on the 3D camera plane is projected to a 3D point on the LiDAR plane, and the distance between the two points are iteratively minimized to estimate the translation matrix. At least three or more planes are used to find accurate external parameters between the coordinate systems. Finally, the estimated transformation is refined using the distance between all chessboard 3D points and the LiDAR plane. In the experiments, quantitative error analysis is done using a simulation tool and real test sequences are also used for calibration consistency analysis.

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“…There is a number of works devoted to the calibration process of video cameras with LiDAR: (Pusztai, Hajder, 2017), (Park et al, 2014), (Pereira et al, 2016), (Xu, Li, 2014), (Guindel et al, 2017), (Chai et al, 2018), (Dhall et al, 2017). All of them could be classified into two groups, according to the sensor orientation method each algorithm is based on.…”

Section: Automatic Camera and Lidar Calibrationmentioning

confidence: 99%

“…Algorithms from the second group are based on the 3D matching for orientation calculation (Guindel et al, 2017), (Chai et al, 2018), (Dhall et al, 2017). Thus, a cameras stereoscopic pair is essential for 3D coordinates calculation of angles on a pair of images.…”

Section: Automatic Camera and Lidar Calibrationmentioning

confidence: 99%

Semantic Scene Understanding for the Autonomous Platform

Vishnyakov

Blokhinov

Sgibnev

et al. 2020

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

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Abstract. In this paper we describe a new multi-sensor platform for data collection and algorithm testing. We propose a couple of methods for solution of semantic scene understanding problem for land autonomous vehicles. We describe our approaches for automatic camera and LiDAR calibration; three-dimensional scene reconstruction and odometry calculation; semantic segmentation that provides obstacle recognition and underlying surface classification; object detection; point cloud segmentation. Also, we describe our virtual simulation complex based on Unreal Engine, that can be used for both data collection and algorithm testing. We collected a large database of field and virtual data: more than 1,000,000 real images with corresponding LiDAR data and more than 3,500,000 simulated images with corresponding LiDAR data. All proposed methods were implemented and tested on our autonomous platform; accuracy estimates were obtained on the collected database.

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A Novel Method for LiDAR Camera Calibration by Plane Fitting

Cited by 33 publications

References 4 publications

Improving Plane Fitting Accuracy with Rigorous Error Models of Structured Light-Based RGB-D Sensors

Improving Plane Fitting Accuracy with Rigorous Error Models of Structured Light-Based RGB-D Sensors

Extrinsic Calibration between Camera and LiDAR Sensors by Matching Multiple 3D Planes

Semantic Scene Understanding for the Autonomous Platform

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