A multi-frame graph matching algorithm for low-bandwidth RGB-D SLAM

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 multiframe 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 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.

An improved GMS-PROSAC algorithm for image mismatch elimination

“…It is well known that image matching plays an important role in this area (Masoud & Hoff, 2016). Some essential requirements of image matching algorithm involve real-time, accuracy as well as robustness (Wang, Zhu, Qin, Jiang, & Li, 2018;Zheng, Hong, Zhang, Li, & Li, 2016). Up to now, there is still a wide performance gap between slow (but robust) feature matchers and the much faster (but often unstable) real-time solutions.…”