Large Area 3D Reconstructions from Underwater Surveys

Robotic underwater vehicles can perform vast optical surveys of the ocean floor. Scientists value these surveys since optical images offer high levels of information and are easily interpreted by humans. Unfortunately the coverage of a single image is limited hy absorption and backscatter while what is needed is an overall view of the survey area. Recent work on underwater mosaics assume planar scenes and are applicable only to Situations without much relief. We present a complete and validated system for processing optical images acquired from an underwater mbotic vehicle to form a 3D reconstruction of the wean floor. Our approach is designed for the most general conditions of wide-baseline imagery (low overlap and presence of significant 3D structure) and scales to hundreds of images. We only assume a calibrated camera system and a vehicle with uncertain and possibly drifting pose information (e.g. a compass, depth sensor and a Doppler velocity Our approach is based on a combination of techniques from computer vision, photogrammetry and mhotics. We use a local to global approach to structure from motion, aided by the navigation sensors on the vehicle to generate 3D suhmaps. These suhmaps are then placed in a common reference frame that is refined by matching overlapping submaps. The final stage of processing is a bundle adjustment that provides the 3D structure, camera poses and uncertainty estimates in a consistent reference frame. We present results with ground-truth for structure as well as results from an oceanographic survey over a coral reef covering an area of appmximately one hundred square meters.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86037/1/opizarro-33.pd

Structure and motion estimation from apparent contours under circular motion

In this paper, we address the problem of recovering structure and motion from the apparent contours of a smooth surface. Fixed image features under circular motion and their relationships with the intrinsic parameters of the camera are exploited to provide a simple parameterization of the fundamental matrix relating any pair of views in the sequence. Such a parameterization allows a trivial initialization of the motion parameters, which all bear physical meaning. It also greatly reduces the dimension of the search space for the optimization problem, which can now be solved using only two epipolar tangents. In contrast to previous methods, the motion estimation algorithm introduced here can cope with incomplete circular motion and more widely spaced images. Existing techniques for model reconstruction from apparent contours are then reviewed and compared. Experiment on real data has been carried out and the 3D model reconstructed from the estimated motion is presented. © 2002 Elsevier Science B.V. All rights reserved.postprin

Enhancing Sensor Measurements through Wide Baseline Stereo Images

In this paper, we suggest an algorithm to enhance the accuracy of sensor measurements representing camera parameters. The process proposed is based solely on a pair of wide baseline (or sparse view) images. We use the so-called JUDOCA operator to extract junctions. This operator produces junctions in terms of locations as well as orientations. Such an information is used to estimate an affine transformation matrix, which is used to guide a variance normalized correlation process that produces a set of possible matches. The fundamental matrix can be easily estimated using the so-called RANSAC scheme. Consequently, the essential matrix can be derived given the available calibration matrix. The essential matrix is then decomposed using Singular Value Decomposition. In addition to a translation vector, this decomposition results in a rotation matrix with accurate rotation angles involved. Mathematical derivation is done to extract angles from the rotation matrix and express them in terms of different rotation systems

Large Area 3-D Reconstructions from Underwater Optical Surveys

Robotic underwater vehicles are regularly performing vast optical surveys of the ocean floor. Scientists value these surveys since optical images offer high levels of detail and are easily interpreted by humans. Unfortunately, the coverage of a single image is limited by absorption and backscatter while what is generally desired is an overall view of the survey area. Recent works on underwater mosaics assume planar scenes and are applicable only to situations without much relief. We present a complete and validated system for processing optical images acquired from an underwater robotic vehicle to form a 3D reconstruction of the ocean floor. Our approach is designed for the most general conditions of wide-baseline imagery (low overlap and presence of significant 3D structure) and scales to hundreds or thousands of images. We only assume a calibrated camera system and a vehicle with uncertain and possibly drifting pose information (e.g., a compass, depth sensor, and a Doppler velocity log). Our approach is based on a combination of techniques from computer vision, photogrammetry, and robotics. We use a local to global approach to structure from motion, aided by the navigation sensors on the vehicle to generate 3D sub-maps. These sub-maps are then placed in a common reference frame that is refined by matching overlapping sub-maps. The final stage of processing is a bundle adjustment that provides the 3D structure, camera poses, and uncertainty estimates in a consistent reference frame. We present results with ground truth for structure as well as results from an oceanographic survey over a coral reef.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86036/1/opizarro-12.pd