Experimental Comparison of Open Source Vision-Based State Estimation Algorithms
| Autor: | Sharmin Rahman, Abhishek Singh, Adem Coskun, Ioannis Rekleitis, Jason M. O'Kane, Md. Modasshir, Alberto Quattrini Li, Shervin Ghasemlou, Marios Xanthidis, A. S. Jagtap, S. M. Doherty |
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| Rok vydání: | 2017 |
| Předmět: |
Estimation
0209 industrial biotechnology Research groups Vision based Computer science 02 engineering and technology Personalization 020901 industrial engineering & automation Open source Computer engineering 0202 electrical engineering electronic engineering information engineering Code (cryptography) Underwater robot 020201 artificial intelligence & image processing State (computer science) |
| Zdroj: | Springer Proceedings in Advanced Robotics ISBN: 9783319501147 ISER |
| DOI: | 10.1007/978-3-319-50115-4_67 |
| Popis: | The problem of state estimation using primarily visual data has received a lot of attention in the last decade. Several open source packages have appeared addressing the problem, each supported by impressive demonstrations. Applying any of these packages on a new dataset however, has been proven extremely challenging. Suboptimal performance, loss of localization, and challenges in customization have not produced a clear winner. Several other research groups have presented superb performance without releasing the code, sometimes materializing as commercial products. In this paper, ten of the most promising open source packages are evaluated, by cross validating them on the datasets provided for each package and by testing them on eight different datasets collected over the years in our laboratory. Indoor and outdoor, terrestrial and flying vehicles, in addition to underwater robots, cameras, and buoys were used to collect data. An analysis on the motions required for the different approaches and an evaluation of their performance is presented. |
| Databáze: | OpenAIRE |
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