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.
The fusion of imaging lidar information and digital imagery results in 2.5-dimensional surfaces covered with texture information, called texel images. These data sets, when taken from different viewpoints, can be combined to create three-dimensional (3-D) images of buildings, vehicles, or other objects. This paper presents a procedure for calibration, error correction, and fusing of flash lidar and digital camera information from a single sensor configuration to create accurate texel images. A brief description of a prototype sensor is given, along with a calibration technique used with the sensor, which is applicable to other flash lidar/digital image sensor systems. The 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. The result is a texel image acquired directly from the sensor. Examples of the resulting images, with improvements from the proposed algorithm, are presented. Results with the prototype sensor show very good match between 3-D points and the digital image (<2.8 image pixels), with a 3-D object measurement error of <0.5% , compared to a noncalibrated error of ∼3% .
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 registra tion 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 (pixel fused 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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