Simultaneous Localization and Mapping
نویسنده
چکیده
The last few years have seen the implementation of autonomous systems in industrial applications that operate in a reliable and consistent manner in areas such as stevedoring, mining, agriculture and a variety of indoor applications. To solve the localisation problem, these systems make use of either absolute information such as GPS, beacons at known positions or available maps of the environment. More complex applications, where no a priori information not external positioning sensors are available, require solving the localisation and mapping problem simultaneously. This problem is usually referred to as Simultaneous Localisation and Mapping (SLAM) or Concurrent Mapping and Localisation (CML). Most real time implementations of SLAM are based on Kalman Filters extended with appropriate models for the vehicle and sensors to solve the SLAM problem. Given that in some scenarios simple vehicle and sensor models are less appropriate, new approaches based on combinations of Kalman filters, particle filters and place recognition algorithms are being used.
منابع مشابه
Map-merging in Multi-robot Simultaneous Localization and Mapping Process Using Two Heterogeneous Ground Robots
In this article, a fast and reliable map-merging algorithm is proposed to produce a global two dimensional map of an indoor environment in a multi-robot simultaneous localization and mapping (SLAM) process. In SLAM process, to find its way in this environment, a robot should be able to determine its position relative to a map formed from its observations. To solve this complex problem, simultan...
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Even when simultaneous localization and mapping (SLAM) solutions have been broadly developed, the vast majority of them relate to a single robot performing measurements in static environments. Researches show that the performance of SLAM algorithms deteriorates under dynamic environments. In this paper, a multi-robot simultaneous localization and mapping (MR-SLAM) system is implemented within a...
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