Abstract
This work presents a non parametric probabilistic mapping based on kernel estimators which does not use grids. The proposed methodology characterizes the map with a cloud of points obtained from several observations of the environment. In order to maintain a bounded number of observations in memory, a recursive subsampling algorithm is proposed. The procedure is included in an SLAM, in order to localize the robot as well. An application example is the presented, where the proposed methodology is applied in an agricultural environment. Simulation and experimentation results are presented to validate the proposal.
Original language | English |
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Title of host publication | 2017 17th Workshop on Information Processing and Control, RPIC 2017 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 1-6 |
Number of pages | 6 |
ISBN (Electronic) | 9789875447547 |
DOIs | |
Publication status | Published - 20 Sept 2017 |
Event | 17th Workshop on Information Processing and Control, RPIC 2017 - Mar del Plata, Argentina Duration: 20 Oct 2017 → 22 Oct 2017 |
Publication series
Name | 2017 17th Workshop on Information Processing and Control, RPIC 2017 |
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Volume | 2017-January |
Conference
Conference | 17th Workshop on Information Processing and Control, RPIC 2017 |
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Country/Territory | Argentina |
City | Mar del Plata |
Period | 20/10/17 → 22/10/17 |
Bibliographical note
Publisher Copyright:© 2017 Comisión Permanente RPIC.
Keywords
- control systems
- estimators
- Mapping
- robotics