Name
Abstract | ||
|---|---|---|
In recent years, reconstructing a sparse map from a simultaneous localization and mapping (SLAM) system on a conventional CPU has undergone remarkable progress. However, obtaining a dense map from the system often requires a high-performance GPU to accelerate computation. This paper proposes a dense mapping approach which can remove outliers and obtain a clean 3D model using a CPU in real-time. The dense mapping approach processes keyframes and establishes data association by using multi-threading technology. The outliers are removed by changing detections of associated vertices between keyframes. The implicit surface data of inliers is represented by a truncated signed distance function and fused with an adaptive weight. A global hash table and a local hash table are used to store and retrieve surface data for data-reuse. Experiment results show that the proposed approach can precisely remove the outliers in scene and obtain a dense 3D map with a better visual effect in real-time. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/JAS.2020.1003357 | IEEE/CAA Journal of Automatica Sinica |
Keywords | DocType | Volume |
sparse map,SLAM,CPU,high-performance GPU,dense mapping,clean 3D model,keyframes,data association,implicit surface data,local hash table,dense 3D map | Journal | 7 |
Issue | ISSN | Citations |
6 | 2329-9266 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weijie Huang | 1 | 2 | 2.38 |
| Guoshan Zhang | 2 | 54 | 8.61 |
| Xiaowei Han | 3 | 1 | 0.35 |