Mr. Xingyin Fu Profile

Xingyin Fu

at Shenyang Institute of Automation

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Author

Publications (4)

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 | 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 | 12 December 2018 Paper

RGB-D dense SLAM with keyframe-based method

Xingyin Fu, Feng Zhu, Qingxiao Wu, Yunlei Sun

Proceedings Volume 10845, 108450J (2018) https://doi.org/10.1117/12.2505258

KEYWORDS: Cameras, Imaging systems, Systems modeling, Visualization, 3D modeling, Motion models, Associative arrays, Optimization (mathematics), Computing systems, Performance modeling

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SPIE Journal Paper | 17 March 2017

Real-time depth camera tracking with geometrically stable weight algorithm

Xingyin Fu, Feng Zhu, Feng Qi, Mingming Wang

OE, Vol. 56, Issue 03, 033104, (March 2017) https://doi.org/10.1117/12.10.1117/1.OE.56.3.033104

KEYWORDS: Cameras, Detection and tracking algorithms, Imaging systems, 3D modeling, Optical tracking, Databases, Systems modeling, Performance modeling, Optical engineering, RGB color model

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