Name
Abstract | ||
|---|---|---|
3D mapping is a fundamental task in robot applications. The traditional mapping methods mainly rely on spatial discretization, in which the amount of data that needs to be stored is large, and the representation ability is limited. As a continuous probability model, the Gaussian Mixture Model (GMM) has a small memory footprint and high-fidelity representation ability. Thus the GMM map is superior ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/FCCM51124.2021.00015 | 2021 IEEE 29th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM) |
Keywords | DocType | ISSN |
Quantization (signal),Navigation,Games,Hardware,Real-time systems,Task analysis,Robots | Conference | 2576-2613 |
ISBN | Citations | PageRank |
978-1-6654-3555-0 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuanfan Xu | 1 | 6 | 2.91 |
| Zhaoliang Zhang | 2 | 0 | 0.68 |
| Jincheng Yu | 3 | 315 | 19.49 |
| Jianfei Cao | 4 | 0 | 0.34 |
| Haolin Dong | 5 | 0 | 1.01 |
| Zhengfeng Huang | 6 | 0 | 0.68 |
| Yu Wang | 7 | 23 | 3.66 |
| Yu Wang | 8 | 2279 | 211.60 |