Abstract
Low-cost, commercial RGB-D cameras have become one of the main sensors for indoor scene 3D perception and robot navigation and localization. In these studies, the Intel RealSense R200 sensor (R200) is popular among many researchers, but its integrated commercial stereo matching algorithm has a small detection range, short measurement distance and low depth map resolution, which severely restrict its usage scenarios and service life. For these problems, on the basis of the existing research, a novel infrared stereo matching algorithm that combines the idea of the semi-global method and sliding window is proposed in this paper. First, the R200 is calibrated. Then, through Gaussian filtering, the mutual information and correlation between the left and right stereo infrared images are enhanced. According to mutual information, the dynamic threshold selection in matching is realized, so the adaptability to different scenes is improved. Meanwhile, the robustness of the algorithm is improved by the Sobel operators in the cost calculation of the energy function. In addition, the accuracy and quality of disparity values are improved through a uniqueness test and sub-pixel interpolation. Finally, the BundleFusion algorithm is used to reconstruct indoor 3D surface models in different scenarios, which proved the effectiveness and superiority of the stereo matching algorithm proposed in this paper.
Highlights
Indoor 3D environment perception technology is one of the key technologies for robot positioning and navigation, virtual reality, augmented reality and indoor mapping and localization [1,2,3,4,5,6,7]
We propose a novel infrared stereo matching algorithm—iff[e1re6n]t Satnerdeo MISaGtchSinMg Aalglgoroitrhmitshm (ISGSM)—to obtain high-quality depth maps for real time indoor 3D perception with the RGB-D sensor
The idea of semi-global matching and a sliding window is adopted, and the mutual information and correlation between binocular infrared images are enhanced by a Gaussian filter, which effectively suppresses image noise
Summary
Indoor 3D environment perception technology is one of the key technologies for robot positioning and navigation, virtual reality, augmented reality and indoor mapping and localization [1,2,3,4,5,6,7]. The RGB-D camera combines the characteristics of two types of sensors, LiDAR and RGB cameras, to obtain point cloud data and RGB image data output in a time series, which is more conducive to real-time acquisition and the update of indoor 3D spatial structure and texture information It is inexpensive compared with devices integrating LiDAR, and covers extensive research and application prospects in close-range indoor 3D perception. The R200 is a representative RGB-D camera based on infrared speckle and stereo vision technology for the depth estimation of indoor scenes Many researchers use it for robot indoor navigation and positioning, indoor 3D Mapping and other research [15]. The following sections of this paper are arranged as follows: Section 2 outlines the research progress of existing stereo vision technology; Section 3 introduces the existing typical algorithms, and describes the newly proposed ISGSM algorithm in detail; Section 4 explains the experimental methods and analyzes the experimental results; Section 5 is the discussion based on the experimental results; and Section 6 is the conclusions
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