Name
Abstract | ||
|---|---|---|
We present a visual simultaneous localization and mapping (SLAM) framework of closing surface loops. It combines both sparse feature matching and dense surface alignment. Sparse feature matching is used for visual odometry and globally camera pose fine-tuning when dense loops are detected, while dense surface alignment is the way of closing large loops and solving surface mismatching problem. To achieve smart dense surface loop closure, a highly efficient CUDA-based global point cloud registration method and a map content dependent loop verification method are proposed. We run extensive experiments on different datasets, our method outperforms state-of-the-art ones in terms of both camera trajectory and surface reconstruction accuracy. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Surface reconstruction,Visual odometry,Pattern recognition,Computer science,CUDA,Feature matching,Artificial intelligence,Point cloud,Simultaneous localization and mapping,Trajectory |
DocType | Volume | Citations |
Journal | abs/1801.01572 | 0 |
PageRank | References | Authors |
0.34 | 10 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guoxiang Zhang | 1 | 0 | 0.68 |
| Yangquan Chen | 2 | 2257 | 242.16 |