Mr. Rongrong Lu Profile

Rongrong Lu

at Shenyang Institute of Automation

SPIE Involvement:

Author

Publications (9)

Proceedings Article | 6 May 2019 Paper

A more repeatable and robust local reference frame for 3D local surface description

Rongrong Lu, Feng Zhu, Qingxiao Wu, Xingyin Fu

Proceedings Volume 11069, 1106913 (2019) https://doi.org/10.1117/12.2524177

KEYWORDS: Object recognition, 3D image processing, Principal component analysis, 3D modeling, 3D scanning

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Proceedings Article | 6 May 2019 Paper

Efficient 3D correspondence grouping by two-stage filtering

Rongrong Lu, Feng Zhu, Qingxiao Wu, Yanzi Kong

Proceedings Volume 11069, 110690Z (2019) https://doi.org/10.1117/12.2524157

KEYWORDS: 3D modeling, 3D image processing, Clouds, Object recognition, 3D scanning

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Proceedings Article | 12 December 2018 Paper

RGB-D dense mapping with feature-based method

Xingyin Fu, Feng Zhu, Qingxiao Wu, Rongrong Lu

Proceedings Volume 10845, 108450K (2018) https://doi.org/10.1117/12.2505305

KEYWORDS: Cameras, Imaging systems, Visualization, Associative arrays, Systems modeling, Matrices, Navigation systems, Autoregressive models, Visual process modeling, 3D modeling

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Simultaneous Localization and Mapping (SLAM) plays an important role in navigation and augmented reality (AR) systems. While feature-based visual SLAM has reached a pre-mature stage, RGB-D-based dense SLAM becomes popular since the birth of consumer RGB-D cameras. Different with the feature-based visual SLAM systems, RGB-D-based dense SLAM systems, for example, KinectFusion, calculate camera poses by registering the current frame with the images raycasted from the global model and produce a dense surface by fusing the RGB-D stream. In this paper, we propose a novel reconstruction system. Our system is built on ORB-SLAM2. To generate the dense surface in real-time, we first propose to use truncated signed distance function (TSDF) to fuse the RGB-D frames. Because camera tracking drift is inevitable, it is unwise to represent the entire reconstruction space with a TSDF model or utilize the voxel hashing approach to represent the entire measured surface. We use moving volume proposed in Kintinuous to represent the reconstruction region around the current frame frustum. Different with Kintinuous which corrects the points with embedded deformation graph after pose graph optimization, we re-fuse the images with the optimized camera poses and produce the dense surface again after the user ends the scanning. Second, we use the reconstructed dense map to filter out the outliers of the features in the sparse feature map. The depth maps of the keyframes are raycasted from the TSDF volume according to the camera pose. The feature points in the local map are projected into the nearest keyframe. If the discrepancy between depth values of the feature and the corresponding point in the depth map exceeds the threshold, the feature is considered as an outlier and removed from the feature map. The discrepancy value is also combined with feature pyramid layer to calculate the information matrix when minimizing the reprojection error. The features in the sparse map reconstructed near the produced dense surface will impose large influence in camera tracking. We compare the accuracy of the produced camera trajectories as well as the 3D models to the state-of-the-art systems on the TUM and ICL-NIUM RGB-D benchmark datasets. Experimental results show our system achieves state-of-the-art results.

Proceedings Article | 10 April 2018 Paper

LSAH: a fast and efficient local surface feature for point cloud registration

Rongrong Lu, Feng Zhu, Qingxiao Wu, Yanzi Kong

Proceedings Volume 10615, 106151G (2018) https://doi.org/10.1117/12.2303809

KEYWORDS: Clouds, Laser range finders, Feature extraction, Statistical modeling, Data modeling, 3D modeling, Image registration, 3D applications, Optical spheres, Performance modeling

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SPIE Journal Paper | 27 December 2017

Three-dimensional object recognition using an extensible local surface descriptor

Rongrong Lu, Feng Zhu, Qingxiao Wu, Yingming Hao

OE, Vol. 56, Issue 12, 123109, (December 2017) https://doi.org/10.1117/12.10.1117/1.OE.56.12.123109

KEYWORDS: Detection and tracking algorithms, 3D modeling, Object recognition, Laser range finders, Optical spheres, Statistical modeling, Optical engineering, Data modeling, Clouds, Feature extraction

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Proceedings Article | 21 July 2017 Paper

Signature of position angles histograms for 3D object recognition

Rongrong Lu, Feng Zhu, Yingming Hao, Huiying Cai, Yinshuai Zhao

Proceedings Volume 10420, 1042045 (2017) https://doi.org/10.1117/12.2281542

KEYWORDS: Object recognition

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SPIE Journal Paper | 9 November 2016

Simple and efficient improvement of spin image for three-dimensional object recognition

Rongrong Lu, Feng Zhu, Yingming Hao, Qingxiao Wu

OE, Vol. 55, Issue 11, 113102, (November 2016) https://doi.org/10.1117/12.10.1117/1.OE.55.11.113102

KEYWORDS: 3D modeling, Object recognition, Laser range finders, 3D image processing, Detection and tracking algorithms, Data modeling, Optical engineering, Statistical modeling, Image resolution, Instrument modeling

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Proceedings Article | 25 October 2016 Paper

Shape matching under affine transformation using normalization and multi-scale area integral features

Huiying Cai, Feng Zhu, Yingming Hao, Rongrong Lu

Proceedings Volume 10157, 101571W (2016) https://doi.org/10.1117/12.2246696

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Proceedings Article | 7 December 2006 Paper

The experimental progress in studying of channeling of charged particles along nanostructure

Zhiyuan Zhu, Dezhang Zhu, Rongrong Lu, Zijian Xu, Wei Zhang, Huihao Xia

Proceedings Volume 5974, 597413 (2006) https://doi.org/10.1117/12.640101

KEYWORDS: Particles, Ions, Carbon nanotubes, Nanostructures, Chemical vapor deposition, Scanning electron microscopy, Backscatter, Carbon, Scattering, Crystals

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Showing 5 of 9 publications

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