No abstract

No 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.

Limited memory resource has always been deemed as the major bottleneck of the program performance, not excepting for dynamic binary translation (DBT) [1] system. However, traditional methods seldom enable this issue mentioned to be solved better due to a fix cache size routinely assigned for codes without considering the program behavior on the fly. Though some prediction methods implemented via LRU [2] histograms can achieve better performance in Operating System, unlike pages in memory, blocks in DBT have unfixed sizes each other, making the prediction imprecise. In this paper, we present a new replace policy named DCC (Dynamic Code Cache) based on working set [3] for bounded code cache, which would dynamically change the size of the code cache and clear the cache according to the information of working set detected. It would sacrifice a small part of the performance to save the space, especially when many great or complicated applications are running on the same physical machine, and the performance might be better under this condition. For Evaluating, we test this strategy on a DBT system named Crossbit [4].We get around 30% space saved up with about 12% running time sacrificed.