SLAMBench2:Multi-Objective Head-to-Head Benchmarking for Visual SLAM
| Autor: | Luigi Nardi, Sajad Saecdi, Harry Wagstaff, Steve Furber, Bruno Bodin, Andrew J. Davison, Michael F. P. O Boyle, Mikel Luján, Emanuele Vespa, John Mawer, Paul H. J. Kelly, Andy Nisbet |
|---|---|
| Přispěvatelé: | Engineering & Physical Science Research Council (E |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Technology Computer science Interface (computing) 02 engineering and technology Simultaneous localization and mapping Machine learning computer.software_genre Computer Science - Robotics 020901 industrial engineering & automation Automation & Control Systems Component (UML) 0202 electrical engineering electronic engineering information engineering Science & Technology business.industry Navigation system Benchmarking Robotics Software framework 020201 artificial intelligence & image processing Augmented reality Artificial intelligence User interface business Robotics (cs.RO) computer |
| Zdroj: | Bodin, B, Wagstaff, H, Saecdi, S, Nardi, L, Vespa, E, Mawer, J, Nisbet, A, Lujan, M, Furber, S, Davison, A J, Kelly, P H J & O'Boyle, M F P 2018, SLAMBench2 : Multi-Objective Head-to-Head Benchmarking for Visual SLAM . in 2018 IEEE International Conference on Robotics and Automation, ICRA 2018 ., 8460558, IEEE, pp. 3637-3644, 2018 IEEE International Conference on Robotics and Automation, Brisbane, Australia, 21/05/18 . https://doi.org/10.1109/ICRA.2018.8460558 IEEE International Conference on Robotics and Automation (ICRA) ICRA Bodin, B, Wagstaff, H, Saeedi, S, Nardi, L, Vespa, E, Mayer, J H, Nisbet, A, Luján, M, Furber, S, Davison, A J, Kelly, P H J & O'Boyle, M 2018, SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM . in The International Conference in Robotics and Automation 2018 . Institute of Electrical and Electronics Engineers (IEEE), Brisbane, QLD, Australia, pp. 3637-3644, 2018 IEEE International Conference on Robotics and Automation, Brisbane, Australia, 21/05/18 . https://doi.org/10.1109/ICRA.2018.8460558 |
| Popis: | SLAM is becoming a key component of robotics and augmented reality (AR) systems. While a large number of SLAM algorithms have been presented, there has been little effort to unify the interface of such algorithms, or to perform a holistic comparison of their capabilities. This is a problem since different SLAM applications can have different functional and non-functional requirements. For example, a mobile phone-based AR application has a tight energy budget, while a UAV navigation system usually requires high accuracy. SLAMBench2 is a benchmarking framework to evaluate existing and future SLAM systems, both open and close source, over an extensible list of datasets, while using a comparable and clearly specified list of performance metrics. A wide variety of existing SLAM algorithms and datasets is supported, e.g. ElasticFusion, InfiniTAM, ORB-SLAM2, OKVIS, and integrating new ones is straightforward and clearly specified by the framework. SLAMBench2 is a publicly-available software framework which represents a starting point for quantitative, comparable and val-idatable experimental research to investigate trade-offs across SLAM systems. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|