Simultaneous localization and mapping (SLAM) is a fundamental problem for various applications. For indoor environments, planes are predominant features that are less affected by measurement noise. In this paper, we propose a novel point-plane SLAM system using RGB-D cameras. First, we extract feature points from RGB images and planes from depth images. Then plane correspondences in the global map can be found using their contours. Considering the limited size of real planes, we exploit constraints of plane edges. In general, a plane edge is an intersecting line of two perpendicular planes. Therefore, instead of line-based constraints, we calculate and generate supposed perpendicular planes from edge lines, resulting in more plane observations and constraints to reduce estimation errors. To exploit the orthogonal structure in indoor environments, we also add structural (parallel or perpendicular) constraints of planes. Finally, we construct a factor graph using all of these features. The cost functions are minimized to estimate camera poses and global map. We test our proposed system on public RGB-D benchmarks, demonstrating its robust and accurate pose estimation results, compared with other state-of-the-art SLAM systems.

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Decolorization is to convert a color image into a gray scale image while preserve image features like salient structure and chrominance contrast. The sign of the color contrast is crucial for the decolorization algorithm and is usually determined in existing works by giving a strict defined color order or twomode weak order. In this paper, a fast computation on color order is achieved via a simple global mapping which is introduced in a linear parametric model using an extended structure transfer filter. The values of the parameters are obtained via an elegant approximation method. A local decolorization algorithm is finally designed on basis of the global linear mapping so that both color and spatial information are preserved robustly and accurately. Experimental results show that the proposed decolorization algorithms obtain a good performance among existing quality metrics for the decolorization. In addition, the proposed global decolorization algorithm is friendly to mobile devices with limited computational resource.

This paper presents a dual-band eight-element multiple-input multiple-output (MIMO) array using a multi-slot decoupling technique for the fifth generation (5G) mobile communication. By employing a compact dual-loop antenna element, the proposed array obtains two broad bandwidths of 12.2% and 15.4% for sub-6GHz operation. To reduce the mutual coupling between antenna elements, a novel dualband decoupling method is proposed by employing a multi-slot structure. The proposed MIMO array achieves 15.5-dB and 19.0-dB isolations across the two operating bands. Furthermore, three decoupling modes generated by different bent slots can be independently tuned. Zero ground clearance is also realized by the coplanar arrangement of the antenna elements and decoupling structures. The proposed MIMO array was simulated, fabricated, and measured. Experimental results agree well with the simulations, showing that the dual-band MIMO array has good impedance matching, high isolation, and high efficiency. In addition, the envelope correlation coefficient and channel capacity are calculated and analyzed to validate the MIMO performance of the 5G terminal array. Such a dual-band high-isolation eight-element MIMO array with zero ground clearance is a promising candidate for 5G or future mobile applications. INDEX TERMS Dual-band decoupling, fifth generation (5G) communication, MIMO antenna, smartphone antenna.

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