Simultaneous Localization and Mapping for Exploration with Stochastic Cloning EKF
| Autor: | Janko Petereit, Thomas Emter |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Current (mathematics) Computer science Mobile robot 02 engineering and technology Simultaneous localization and mapping Measure (mathematics) Extended Kalman filter 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing State (computer science) Algorithm Search and rescue Factor graph |
| Zdroj: | SSRR |
| DOI: | 10.1109/ssrr.2019.8848937 |
| Popis: | While exploring unknown territory on search and rescue missions, fusing multiple sensors is vital for the precise on-line localization of mobile robots. The Extended Kalman filter (EKF) with stochastic cloning is well suited for this purpose and allows to directly integrate multiple absolute and relative state measurements. The latter measure differences between a past state and the current state, thus introducing correlations. These inter-dependencies are modeled by stochastic cloning, which performs a state augmentation by cloning the respective state estimates connected by a relative measurement. Different approaches to feed back information from absolute updates to the cloned state are presented and compared to a SLAM algorithm based on factor graph optimization. |
| Databáze: | OpenAIRE |
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