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12
View planning for automated threedimensional object reconstruction . . .
, 2003
"... Laser scanning range sensors are widely used for highprecision, highdensity threedimensional (3D) reconstruction and inspection of the surface of physical objects. The process typically involves planning a set of views, physically altering the relative objectsensor pose, taking scans, registerin ..."
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Cited by 72 (0 self)
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Laser scanning range sensors are widely used for highprecision, highdensity threedimensional (3D) reconstruction and inspection of the surface of physical objects. The process typically involves planning a set of views, physically altering the relative objectsensor pose, taking scans, registering the acquired geometric data in a common coordinate frame of reference, and finally integrating range images into a nonredundant model. Efficiencies could be achieved by automating or semiautomating this process. While challenges remain, there are adequate solutions to semiautomate the scanregisterintegrate tasks. On the other hand, view planning remains an open problem—that is, the task of finding a suitably small set of sensor poses and configurations for specified reconstruction or inspection goals. This paper surveys and compares view planning techniques for automated 3D object reconstruction and inspection by means of active, triangulationbased range sensors.
A multiresolution ICP with heuristic closest Point search for fast and robust 3D Registration of range images
 in Proc. IEEE Conf. on 3D Imaging and Modeling
, 2003
"... The iterative closest point (ICP) algorithm is widely used for the registration of 3D geometric data. One of the main drawbacks of the algorithm is its quadratic time complexity O(N 2) with the number of points N. Consequently, several methods have been proposed to accelerate the process. This paper ..."
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Cited by 28 (1 self)
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The iterative closest point (ICP) algorithm is widely used for the registration of 3D geometric data. One of the main drawbacks of the algorithm is its quadratic time complexity O(N 2) with the number of points N. Consequently, several methods have been proposed to accelerate the process. This paper presents a new solution for the speeding up of the ICP algorithm and special care is taken to avoid any tradeoff with the quality of the registration. The proposed solution combines a coarse to fine multiresolution approach with the neighbor search algorithm. The multiresolution approach permits to successively improve the registration using finer levels of representation and the neighbor search algorithm speeds up the closest point search by using a heuristic approach. Both multiresolution scheme and neighbor search algorithm main features are presented in this paper. Confirming the success of the proposed solution, typical results show that this combination permits to create a very fast ICP algorithm, with a closest point search complexity of O(N), while preserving the matching quality. 1.
Parallel Evolutionary Registration of Range Data
 COMPUTER VISION AND IMAGE UNDERSTANDING
, 2002
"... ... This paper reports on an evolutionary registration algorithm which does not require initial prealignment and has a very broad basin of convergence ..."
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Cited by 21 (2 self)
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... This paper reports on an evolutionary registration algorithm which does not require initial prealignment and has a very broad basin of convergence
Fast correspondence search for 3D surface matching
 International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36 (Part 3/W19
, 2005
"... An algorithm for least squares matching of overlapping 3D surfaces is presented. It estimates the transformation parameters between two or more fully 3D surfaces, using the Generalized GaussMarkoff model, minimizing the sum of squares of the Euclidean distances between the surfaces. This formulatio ..."
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Cited by 12 (7 self)
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An algorithm for least squares matching of overlapping 3D surfaces is presented. It estimates the transformation parameters between two or more fully 3D surfaces, using the Generalized GaussMarkoff model, minimizing the sum of squares of the Euclidean distances between the surfaces. This formulation gives the opportunity of matching arbitrarily oriented 3D surfaces simultaneously, without using explicit tie points. Besides the mathematical model and execution aspects we pay particular interest to the reduction of the computational expenses. An efficient space partitioning method is implemented in order to speed up the correspondence search, which is the main portion of the computational efforts. The simultaneous matching of subsurface patches is given as another strategy. It provides a computationally effective solution, since it matches only relevant multisubpatches rather then the whole overlapping area. A practical example including computation times is given for the demonstration of the method. 1.
Parallel Alignment of a Large Number of Range Images
 PROC. OF THE 4TH INTERNATIONAL CONFERENCE ON 3D DIGITAL IMAGING AND MODELLING, 2003
, 2003
"... This paper describes a method for parallel alignment of multiple range images. It is difficult to align a large number of range images simultaneously. Therefore, we developed the parallel method to improve the time and memory performances of the alignment process. Although a general simultaneous ali ..."
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Cited by 7 (2 self)
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This paper describes a method for parallel alignment of multiple range images. It is difficult to align a large number of range images simultaneously. Therefore, we developed the parallel method to improve the time and memory performances of the alignment process. Although a general simultaneous alignment algorithm searches correspondences for all pairs of all range images by rejecting redundant dependencies, our method makes it possible to accelerate computation time and reduce the amount of memory used. Since the computation between two range images can be preformed independently, each correspondence pair of range images is assigned to each node. Because the computation time is proportional to the number of vertices assigned to each node, by assigning the pairs so that the number of vertices computed is equal on each node, the load on each node is effectively distributed. The heuristic algorithms for graph partitioning are applied to this problem in order to reduce the amount of memory used on each node. The method was tested on a 16 processor PC cluster, where it demonstrated the high extendibility and the performance improvement in time and memory.
Discrete pose space estimation to improve icpbased tracking. 3DIM
, 2005
"... Iterative Closest Point (ICP)based tracking works well when the interframe motion is within the ICP minimum well space. For large interframe motions resulting from a limited sensor acquisition rate relative to the speed of the object motion, it suffers from slow convergence and a tendency to be sta ..."
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Cited by 5 (0 self)
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Iterative Closest Point (ICP)based tracking works well when the interframe motion is within the ICP minimum well space. For large interframe motions resulting from a limited sensor acquisition rate relative to the speed of the object motion, it suffers from slow convergence and a tendency to be stalled by local minima. A novel method is proposed to improve the performance of ICPbased tracking. The method is based upon the Bounded Hough Transform (BHT) which estimates the object pose in a coarse discrete pose space. Given an initial pose estimate, and assuming that the interframe motion is bounded in all 6 pose dimensions, the BHT estimates the current frame’s pose. On its own, the BHT is able to track an object’s pose in sparse range data both efficiently and reliably, albeit with a limited precision. Experiments on both simulated and real data show the BHT to be more efficient than a number of variants of the ICP for a similar degree of reliability. A hybrid method has also been implemented wherein at each frame the BHT is followed by a few ICP iterations. This hybrid method is more efficient than the ICP, and is more reliable than either the BHT or ICP separately. 1.
Parallelization of Scan Matching for Robotic 3D Mapping
"... Abstract — Robotic 3D Mapping of environments is computationally expensive, since 3D scanners sample the environment with many data points. In addition, the solution space grows exponentially with the additional degrees of freedom needed to represent the robot pose. Mapping environments in 3D must r ..."
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Cited by 4 (0 self)
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Abstract — Robotic 3D Mapping of environments is computationally expensive, since 3D scanners sample the environment with many data points. In addition, the solution space grows exponentially with the additional degrees of freedom needed to represent the robot pose. Mapping environments in 3D must regard six degrees of freedom to characterize the robot pose. This paper extends our solution to the 3D mapping problem by parallelization. The availability of multicore processors as well as efficient programming schemes as OpenMP permit the parallel execution of robotics task with onboard means. Index Terms — 3D scan matching, Iterative Closest Point Algorithm,
3D reconstruction of environments for planetary exploration
 in 'Computer and Robot Vision, 2005. Proceedings. The 2nd Canadian Conference on
, 2005
"... In this paper we present our approach to 3D surface reconstruction from large sparse range data sets. In space robotics constructing an accurate model of the environment is very important for a variety of reasons. In particular, the constructed model can be used for: safe teleoperation, path planni ..."
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Cited by 3 (1 self)
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In this paper we present our approach to 3D surface reconstruction from large sparse range data sets. In space robotics constructing an accurate model of the environment is very important for a variety of reasons. In particular, the constructed model can be used for: safe teleoperation, path planning, planetary exploration and mapping of points of interest. Our approach is based on acquiring range scans from different viewpoints with overlapping regions, merge them together into a single data set, and fit a triangular mesh on the merged data points. We demonstrate the effectiveness of our approach in a path planning scenario and also by creating the accessibility map for a portion of the Mars Yard located in
Realtime Object Recognition in Sparse Range Images Using Error Surface Embedding
, 2010
"... In this work we address the problem of object recognition and localization from sparse range data. The method is based upon comparing the 7D error surfaces of objects in various poses, which result from the registration error function between two convolved surfaces. The objects and their pose value ..."
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Cited by 3 (1 self)
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In this work we address the problem of object recognition and localization from sparse range data. The method is based upon comparing the 7D error surfaces of objects in various poses, which result from the registration error function between two convolved surfaces. The objects and their pose values are encoded by a small set of feature vectors extracted from the minima of the error surfaces. The problem of object recognition is thus reduced to comparing these feature vectors to find the corresponding error surfaces between the runtime data and a preprocessed database. Specifically, we present a new approach to the problems of pose determination, object recognition and object class recognition. The algorithm has been implemented and tested on both simulated and real data. The experimental results demonstrate the technique to be both effective and efficient, executing at 122 frames per second on standard hardware and with recognition rates exceeding 97 % for a database of 60 objects. The performance of the proposed potential well space embedding (PWSE) approach on large size databases was also evaluated on the Princeton Shape Bench