Journal of System Simulation
Abstract
Abstract: To solve the problems of point cloud noise, depth data mismatching and local details prone to hollowing in the process of Kinect camera reconstruction of indoor three-dimensional environment. By improving the assignment method of depth image pixel and the threshold relationship of time frame matching, this paper denoises the point cloud with optimizing mean filtering and time frame weighting methods. Combined with recent iteration algorithm (Iterative Closest Point, ICP) to complete the adjacent point cloud splicing so that it can achieve completely indoor model to build 3D environment. To test the validity of the algorithm, the experiment of reconstructing indoor environment with Kinect camera is designed. The experimental results show that by using the improved mean filter and time frame weighted method, the point cloud model quality loss compared to that before noise reduction falls by 3.33% on average. Point cloud signal-to-noise ratio is improved compared to that before noise reduction processing before by 2.18dB, which is given to illustrate the feasibility of these two optimization algorithms.
Recommended Citation
Xu, Lianrui and Zhang, Jinming
(2019)
"Modeling Indoor environment with Kinect,"
Journal of System Simulation: Vol. 31:
Iss.
12, Article 10.
DOI: 10.16182/j.issn1004731x.joss.19-FZ0439
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol31/iss12/10
First Page
2643
Revised Date
2019-08-24
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.19-FZ0439
Last Page
2651
CLC
TP391.9
Recommended Citation
Xu Lianrui, Zhang Jinming. Modeling Indoor environment with Kinect[J]. Journal of System Simulation, 2019, 31(12): 2643-2651.
DOI
10.16182/j.issn1004731x.joss.19-FZ0439
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