Name
Abstract | ||
|---|---|---|
Real-time dense computer vision and SLAM offer great potential for a new level of scene modelling, tracking and real environmental interaction for many types of robot, but their high computational requirements mean that use on mass market embedded platforms is challenging. Meanwhile, trends in low-cost, low-power processing are towards massive parallelism and heterogeneity, making it difficult for robotics and vision researchers to implement their algorithms in a performance-portable way. In this paper we introduce SLAMBench, a publicly-available software framework which represents a starting point for quantitative, comparable and validatable experimental research to investigate trade-offs in performance, accuracy and energy consumption of a dense RGB-D SLAM system. SLAMBench provides a KinectFusion implementation in C++, OpenMP, OpenCL and CUDA, and harnesses the ICL-NUIM dataset of synthetic RGB-D sequences with trajectory and scene ground truth for reliable accuracy comparison of different implementation and algorithms. We present an analysis and breakdown of the constituent algorithmic elements of KinectFusion, and experimentally investigate their execution time on a variety of multicore and GPU-accelerated platforms. For a popular embedded platform, we also present an analysis of energy efficiency for different configuration alternatives. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICRA.2015.7140009 | IEEE International Conference on Robotics and Automation |
Keywords | DocType | Volume |
C++ language,SLAM (robots),control system analysis computing,image colour analysis,image sequences,parallel architectures,public domain software,robot vision,C++,CUDA,ICL-NUIM dataset,KinectFusion,OpenCL,OpenMP,RGB-D SLAM system,SLAMBench,accuracy benchmarking methodology,energy consumption,publicly-available software framework,simultaneous localisation and mapping,synthetic RGB-D sequences | Journal | abs/1410.2167 |
Issue | ISSN | Citations |
1 | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7140009
IEEE Xplore 2015 | 39 |
PageRank | References | Authors |
1.38 | 17 | 12 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luigi Nardi | 1 | 76 | 6.94 |
| Bruno Bodin | 2 | 49 | 2.94 |
| M. Zeeshan Zia | 3 | 173 | 7.36 |
| John Mawer | 4 | 64 | 4.50 |
| Andy Nisbet | 5 | 265 | 23.55 |
| Paul H. J. Kelly | 6 | 1361 | 112.65 |
| Andrew J. Davison | 7 | 6707 | 350.85 |
| Mikel Luján | 8 | 167 | 20.01 |
| Michael F. P. O'Boyle | 9 | 43 | 4.20 |
| Graham D. Riley | 10 | 39 | 2.39 |
| Nigel Topham | 11 | 39 | 1.38 |
| S. B. Furber | 12 | 1484 | 179.05 |