Calibration method for texel images created from fused flash lidar and digital camera images

Budge, Scott E.; Badamikar, Neeraj S.

doi:10.1117/1.oe.52.10.103101

Cited by 11 publications

(12 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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“…Additional details are given in the literature. 31 The camera is constructed to directly produce fused datasets-no post processing is necessary. This is possible because each 3-D lidar measurement obtained from the lidar sensor array is assigned, through a calibration process, to a pixel in the simultaneously captured EO image.…”

Section: Texel Cameramentioning

confidence: 99%

Automatic registration of fused lidar/digital imagery (texel images) for three-dimensional image creation

2014

Self Cite

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Several photogrammetry-based methods have been proposed that the derive three-dimensional (3-D) 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 a lack of proper convergence in the merging process. This paper presents a method to create 3-D images that uses the unique properties of texel images (pixel-fused lidar and digital imagery) to improve the quality and robustness of fused 3-D 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 3-D points are fused at the sensor level, more accurate 3-D 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. The proposed method also includes modifications for the situation where an estimate of position and attitude of the sensor is known, when obtained from low-cost global positioning systems and inertial measurement units sensors.

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“…The registration is accomplished through a calibration process which involves mapping each ladar point to a point on the EO image, and does not change from capture to capture. 8 This paper describes a triangulation method that incorporates both 2D EO image data and 3D ladar measurements from a low-cost, small UAV to create TDEMs. As the small UAV flies in a straight and level flight profile, the ladar scans a series of shots perpendicular (cross-track) to the flight path, as well as a narrow strip or swath of EO imagery, as illustrated in Figure 1 image are called texel swaths.…”

Section: Introductionmentioning

confidence: 99%

Textured digital elevation model formation from low-cost UAV LADAR/digital image data

Bybee

Budge

2015

SPIE Proceedings

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Textured digital elevation models (TDEMs) have valuable use in precision agriculture, situational awareness, and disaster response. However, scientific-quality models are expensive to obtain using conventional aircraft-based methods. The cost of creating an accurate textured terrain model can be reduced by using a low-cost (<$20k) UAV system fitted with ladar and electro-optical (EO) sensors.A texel camera fuses calibrated ladar and EO data upon simultaneous capture, creating a texel image. This eliminates the problem of fusing the data in a post-processing step and enables both 2D-and 3D-image registration techniques to be used. This paper describes formation of TDEMs using simulated data from a small UAV gathering swaths of texel images of the terrain below. Being a low-cost UAV, only a coarse knowledge of position and attitude is known, and thus both 2D-and 3D-image registration techniques must be used to register adjacent swaths of texel imagery to create a TDEM.The process of creating an aggregate texel image (a TDEM) from many smaller texel image swaths is described. The algorithm is seeded with the rough estimate of position and attitude of each capture. Details such as the required amount of texel image overlap, registration models, simulated flight patterns (level and turbulent), and texture image formation are presented. In addition, examples of such TDEMs are shown and analyzed for accuracy.

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Calibration method for texel images created from fused flash lidar and digital camera images

Cited by 11 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

Automatic registration of fused lidar/digital imagery (texel images) for three-dimensional image creation

Textured digital elevation model formation from low-cost UAV LADAR/digital image data

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