A calibration-and-error correction method for improved texel (fused ladar/digital camera) images

Budge, Scott E.

doi:10.1117/12.919411

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…Pandey et al [15] propose a method for extrinsic calibration of a LiDAR and camera system using the maximization of mutual information between surface intensities. Budge et al [16,17] propose a TOF flash LiDAR and camera method using the chessboard pattern. Their method combines systematic error correction of the flash LiDAR data, correction for lens distortion of the digital camera and flash LiDAR images, and fusion of the LiDAR to the camera data in a single process.…”

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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Section: Introductionmentioning

confidence: 99%

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

Kim

Park

2019

Sensors

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“…To evaluate the performance of the proposed method, the ICP and the proposed method among the estimation algorithms used in a previous study [19] was compared. ICP has the following characteristics: first, the ICP is widely used for three-dimensional motion alignment.…”

Section: Resultsmentioning

confidence: 99%

“…However, this method requires an expensive equipment, the RTK-GPS, to estimate the object information error and could not cope with the coordinate mismatch occurring in real-time. Another approach is minimizing the position error between sensor measurements based on the error estimation methods, such as iterative closest point (ICP), least square method (LSM), amongst other methods [17,18,19,20,21]. One of the previous studies in this approach uses lidar, which has a position accuracy of less than 0.1m, as the reference sensor and correcting the other sensors with ICP algorithm.…”

Section: Introductionmentioning

confidence: 99%

Fast Online Coordinate Correction of a Multi-Sensor for Object Identification in Autonomous Vehicles

Lee

Sunwoo

et al. 2019

Sensors

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Multi-sensor perception systems may have mismatched coordinates between each sensor even if the sensor coordinates are converted to a common coordinate. This discrepancy can be due to the sensor noise, deformation of the sensor mount, and other factors. These mismatched coordinates can seriously affect the estimation of a distant object’s position and this error can result in problems with object identification. To overcome these problems, numerous coordinate correction methods have been studied to minimize coordinate mismatching, such as off-line sensor error modeling and real-time error estimation methods. The first approach, off-line sensor error modeling, cannot cope with the occurrence of a mismatched coordinate in real-time. The second approach, using real-time error estimation methods, has high computational complexity due to the singular value decomposition. Therefore, we present a fast online coordinate correction method based on a reduced sensor position error model with dominant parameters and estimate the parameters by using rapid math operations. By applying the fast coordinate correction method, we can reduce the computational effort within the necessary tolerance of the estimation error. By experiments, the computational effort was improved by up to 99.7% compared to the previous study, and regarding the object’s radar the identification problems were improved by 94.8%. We conclude that the proposed method provides sufficient correcting performance for autonomous driving applications when the multi-sensor coordinates are mismatched.

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“…12 For this constraint to hold, the EO images must be calibrated to remove nonlinear distortion. 17 A geometric interpretation of the constraint is illustrated in Figure 5. Any 3D point x visible in two images will project a point onto the image planes (given by u and u in the figure).…”

Section: Random Sampling Consensus (Ransac)mentioning

confidence: 99%

Automatic registration of multiple texel images (fused lidar/digital imagery) for 3D image creation

Budge¹,

Badamikar²

2013

SPIE Proceedings

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Creation of 3D images through remote sensing is a topic of interest in many applications such as terrain/building modeling and automatic target recognition (ATR). Several photogrammetry-based methods have been proposed that derive 3D information from digital images from different perspectives, and lidar-based methods have been proposed that merge lidar point clouds and texture the merged point clouds with digital imagery. Image registration alone has difficulty with smooth regions with low contrast, whereas point cloud merging alone has difficulty with outliers and lack of proper convergence in the merging process. This paper presents a method to create 3D images that uses the unique properties of texel images (pixelfused lidar and digital imagery) to improve the quality and robustness of fused 3D images. The proposed method uses both image processing and point-cloud merging to combine texel images in an iterative technique. Since the digital image pixels and the lidar 3D points are fused at the sensor level, more accurate 3D images are generated because registration of image data automatically improves the merging of the point clouds, and vice versa. Examples illustrate the value of this method over other methods.

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A calibration-and-error correction method for improved texel (fused ladar/digital camera) images

Cited by 4 publications

References 13 publications

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

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

Fast Online Coordinate Correction of a Multi-Sensor for Object Identification in Autonomous Vehicles

Automatic registration of multiple texel images (fused lidar/digital imagery) for 3D image creation

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