Resource-Aware Large-Scale Cooperative Three-Dimensional Mapping Using Multiple Mobile Devices
| Autor: | Joel A. Hesch, Ruipeng Li, Georgios A. Georgiou, Chao X. Guo, Stergios I. Roumeliotis, Kourosh Sartipi, Esha D. Nerurkar, Ryan C. DuToit, John O'Leary |
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| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Computer science 02 engineering and technology computer.software_genre Computer Science Applications Visualization 020901 industrial engineering & automation Transformation (function) Control and Systems Engineering Server Line (geometry) 0202 electrical engineering electronic engineering information engineering Trajectory 020201 artificial intelligence & image processing Point (geometry) Data mining Electrical and Electronic Engineering Scale (map) Mobile device computer |
| Zdroj: | IEEE Transactions on Robotics. 34:1349-1369 |
| ISSN: | 1941-0468 1552-3098 |
| DOI: | 10.1109/tro.2018.2858229 |
| Popis: | In this paper, we address the problem of cooperative mapping (CM) using datasets collected by multiple users at different times, when the transformation between the users’ starting poses is unknown. Specifically, we formulate CM as a constrained optimization problem, in which each user's independently estimated trajectory and map are merged together by imposing geometric constraints between commonly observed point and line features. Additionally, we provide an algorithm for efficiently solving the CM problem, by taking advantage of its structure. The proposed solution is proven to be batch-least-squares (BLS) optimal over all users’ datasets, while it is less memory demanding and lends itself to parallel implementations. In particular, our solution is shown to be faster than the standard BLS solution, when the overlap between the users’ data is small. Furthermore, our algorithm is resource-aware as it is able to consistently trade accuracy for lower processing cost, by retaining only an informative subset of the common-feature constraints. Experimental results based on visual and inertial measurements collected from multiple users within large buildings are used to assess the performance of the proposed CM algorithm. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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