3D Modeling from Multiple Views with Integrated Registration and Data Fusion

Boyer, Alain; Curtis, Phillip; Payeur, Pierre

doi:10.1109/crv.2009.39

Cited by 4 publications

(4 citation statements)

References 22 publications

(20 reference statements)

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“…Our preliminary datasets include data acquired from an inhouse 3D stereo acquisition system described in [7]. When reading the point data, all information other than positional data was removed and the points order randomized to ensure optimal performance from the Delaunay algorithm.…”

Section: Resultsmentioning

confidence: 99%

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A method to segment a 3D surface point cloud for selective sensing in robotic exploration

Curtis

Payeur

2010

2010 IEEE International Workshop on Robotic and Sensors Environments

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Autonomous robotic exploration in a 3D environment requires the acquisition of 3D data to create a consistent internal model of the environment from which objects can be recognized for the robot to interact with. As the acquisition of 3D data with stereo vision or a laser range finder can be a relatively long process, selective sensing is desired to optimize the amount of data collected to accurately represent the environment in a minimal amount of time. In order to perform selective sensing, a coarse acquisition of the environment first needs to take place. Regions of interest, such as edges and other boundaries, can then be identified so that an acquisition with higher spatial resolution can occur over bounded regions.For that purpose a segmentation method of the coarse data is proposed so that regions can be efficiently distinguished from each other. The method takes a raw 3D surface profile point cloud of varying point densities, organizes it into a mesh, and then segments the surfaces present in this point cloud, producing a segmented mesh, as well as an octree of labeled voxels corresponding to the segmentation. This mesh and octree may then be used for sensory selection to drive a robot exploration task. The method is demonstrated on actual datasets collected in a laboratory environment.

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Section: Resultsmentioning

confidence: 99%

“…The proposed technique deals with 3D data acquired from an in-house structured light stereoscopic system [7] which operates under many different conditions, and can acquire data over several types of surfaces. Delaunay triangulation is initially applied on the raw 3D point cloud to produce a surface profile.…”

Section: Overview Of Proposed Techniquementioning

confidence: 99%

A method to segment a 3D surface point cloud for selective sensing in robotic exploration

Curtis

Payeur

2010

2010 IEEE International Workshop on Robotic and Sensors Environments

Self Cite

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show abstract

“…A detailed discussion of the 3D sensing systems used for acquiring the shape of the automotive body panels is beyond the scope of this chapter. For laboratory evaluation, a custom structured light sensor (Boyer 2009;Boyer et al 2009;Payeur & Desjardins 2009) is used in combination with slightly enlarged dings and dents deformations artificially affixed on the test panels. Alternatively, higher resolution datasets collected by industrial partners with an active laser range sensor on real automotive panels is also used to demonstrate the capability of adaptation of the proposed approach to different scales and its independence from the 3D acquisition system.…”

Section: Data Setsmentioning

confidence: 99%

“…One of several real or imitation automotive panels is mounted on the sled system to imitate a real automotive panel. At the beginning of the assembly line, when the automotive panel is static, a structured light sensor is used to generate a dense 3D reconstruction of the surface of the automotive panel (Boyer 2009;Boyer et al 2009). The deformation detection subsystem processes this 3D data, and acquires the location of the deformations.…”

Section: Experimental Platform and Setupmentioning

confidence: 99%