3D Reconstruction Using Low Precision Scanner

Takimoto, Rogério Y.; Vogelaar, Renato; Ueda, Edson Kenji; Sato, Ayami; Martins, Thiago de Castro; Gotoh, Toshiyuki; Kagei, Seiichiro; Tsuzuki, Marcos de Sales Guerra

doi:10.3182/20130522-3-br-4036.00026

Cited by 9 publications

(6 citation statements)

References 14 publications

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“…This work improves the research from Takimoto et al [6], [7] by modifying the color information usage. They used the color information to calculate the error in the point correspondence (color and geometric information).…”

Section: Introductionmentioning

confidence: 59%

“…PROPOSED METHOD OVERVIEW Figure 1 shows that two sequentially acquired point clouds P C i and P C i+1 are registered by determining the transformation T i . This is different from the method proposed by Takimoto et al [7] in which after the registration, both point clouds are merged and then the following acquired point cloud is registered with the merged point cloud. The approach presented in Fig.…”

Section: Introductionmentioning

confidence: 97%

“…The proposed methods [3]- [5] presented results with two point clouds. Takimoto et al [6], [7] proposed an ICP that uses color information to improve point correspondence and point cloud registration. The color is used in the error calculation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Shape reconstruction from multiple RGB-D point cloud registration

Takimoto¹,

Tsuzuki²,

Vogelaar³

et al. 2014

2014 12th IEEE International Conference on Industrial Informatics (INDIN)

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The objective of this work is to present an object 3D reconstruction method using the point color information. The object 3D reconstruction is performed by combining point clouds obtained from different viewpoints using two cameras and a structured light projector. The main task is the point cloud registration algorithm that matches two point clouds. A well known algorithm for point cloud registration is the ICP (Iterative Closest Point) that determines the rotation and translation that when applied to one of the point clouds, place both point clouds in accordance. The ICP algorithm executes iteratively two main steps: point correspondence determination and registration algorithm. The point correspondence determination is a module that if not executed properly can make the ICP to converge to a local minimum. To overcome such drawback an ICP that uses statistics to generate a dynamic distance and color threshold on the distance allowed between closest points is proposed and implemented. This approach allows subset matches, instead of matching all points from the point clouds. The surface reconstruction is performed using Marching Cubes and a consensus surface algorithm with signed distance compensates point cloud errors. In this paper the performance of the proposed method is analyzed and compared with the classical ICP.

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Section: Introductionmentioning

confidence: 59%

Section: Introductionmentioning

confidence: 97%

See 1 more Smart Citation

Shape reconstruction from multiple RGB-D point cloud registration

Takimoto¹,

Tsuzuki²,

Vogelaar³

et al. 2014

2014 12th IEEE International Conference on Industrial Informatics (INDIN)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our approach, to fuse all relative location data, is similar to the one reported in the literature for fusing multiple 3D point clouds. 82 Namely, in order to fuse the relative-location data, transformation parameters for individual robot data sets are optimized sequentially to improve the superimposition. At each optimization step, a single set of transformation parameters is selected for optimization, leaving all others fixed.…”

Section: Swarm-topology Estimationmentioning

confidence: 99%

“…Namely, the group topologies are superimposed in a way that minimizes cluster sizes of overlapping robots. Our optimization procedure is similar to the one described in the work of Takimoto et al, 82 as discussed above.…”

Section: Swarm-topology Estimationmentioning

confidence: 99%

A high-performance millirobot for swarm-behaviour studies: Swarm-topology estimation

Kim

Kashino

Pineros

et al. 2019

International Journal of Advanced Robotic Systems

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In this article, we present a novel high-performance millirobot ( milli- robot- Toronto), designed to allow for the testing of complex swarm-behaviours, including human–swarm interaction. milli- robot- Toronto, built only with off-the-shelf components, has locomotion, processing and sensing capabilities that significantly improve upon existing designs, while maintaining one of the smallest footprints among current millirobots. As complementary software to this hardware development, herein, we also present a new global swarm-topology estimation algorithm. The method is novel in that it uniquely fuses incomplete location data collected by the individual robots in a distributed manner to optimally estimate the topology of the overall swarm using a centralized computer. It is a generalized technique usable by any swarm comprising robots capable of collecting location estimates of neighbouring robots. Numerous experiments, evaluating the performance of milli- robot- Toronto and the proposed optimal swarm-topology estimation algorithm, are also included.

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