A multi-frame graph matching algorithm for low-bandwidth RGB-D SLAM. (September 2016)

• Record Type: Journal Article
• Authors: Zheng, Shuai; Hong, Jun; Zhang, Kang; Li, Baotong; Li, Xin
• Is Part Of: Computer aided design. Volume 78(2016)
• Page Start: 107
• Page End: 117
• Publication Date: 2016-09
• Subjects: Multi-frame graph matching -- Partial matching -- Low-bandwidth SLAM -- RGB-D reconstruction; Computer-aided design -- Periodicals; Engineering design -- Data processing -- Periodicals; Computer graphics -- Periodicals; Conception technique -- Informatique -- Périodiques; Infographie -- Périodiques; Computer graphics; Engineering design -- Data processing; Periodicals; Electronic journals; 620.00420285
• DOI: 10.1016/j.cad.2016.05.009
• Languages: English
• ISSNs: 0010-4485

Abstract

Abstract: This paper presents a novel multi-frame graph matching algorithm for reliable partial alignments among point clouds. We use this algorithm to stitch frames for 3D environment reconstruction. The idea is to utilize both descriptor similarity and mutual spatial coherency of features existed in multiple frames to match these frames. The proposed multi-frame matching algorithm can extract coarse correspondence among multiple point clouds more reliably than pairwise matching algorithms, especially when the data are noisy and the overlap is relatively small. When there are insufficient consistent features that appeared in all these frames, our algorithm reduces the number of frames to match to deal with it adaptively. Hence, it is particularly suitable for cost-efficient robotic Simultaneous Localization and Mapping (SLAM). We design a prototype system integrating our matching and reconstruction algorithm on a remotely controlled navigation iRobot, equipped with a Kinect and a Raspberry Pi. Our reconstruction experiments demonstrate the effectiveness of our algorithm and design. Highlights: A novel multi-frame graph matching algorithm for reliable partial matching. An online SLAM algorithm for noisy RGBD data sequence with sparse sampling rate. A prototype system using iRobot, Raspberry Pi, and Kinect for 3D indoor mapping.