Name
Abstract | ||
|---|---|---|
Initial map determines the effect of followed slam tracking. Most feature-based monocular slam initialize their map according to key points matching in close frames. Nevertheless, it will consume lots of computational resources and time. And it is easy to fail in some far scene or close scene. In this paper, we present a fast initialization method to reduce runtime and improve success rate of initialization for feature-based monocular slam. First, vanishing points detection based on line segment detector [1] is adopted. Second, we extract orb key points. And the coordinates of every key points are undistorted and normalized. Third, we generate the corresponding depth for each key point by normalizing its distance to the existing vanishing points or gaussian random number. We compare our method with state-of-the-art on public data sets and ours. The experiments show that our method outperforms on runtime and accuracy. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | fast initialization, feature-based, monocular slam, vanishing points |
Field | DocType | ISSN |
Computer vision,Data set,Computer science,Orb (optics),Image segmentation,Feature extraction,Gaussian,Artificial intelligence,Initialization,Simultaneous localization and mapping,Vanishing point | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| shaobo zhang | 1 | 2 | 2.19 |
| Sheng Liu | 2 | 5 | 8.58 |
| Jianhua Zhang | 3 | 25 | 5.97 |
| Zhenhua Wang | 4 | 12 | 3.23 |
| Xiaoyan Wang | 5 | 20 | 8.83 |