Self-localization of mobile robot in unknown environment
| Autor: | Andrew Priorov, Alexandr Prozorov, Ilya Lebedev, Alexandr Tyukin |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
particle filter
Robot kinematics Robot calibration business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Monte Carlo localization computer science slam Mobile robot Kalman filter Simultaneous localization and mapping unscented Kalman filter computer vision lcsh:Telecommunication Computer Science::Robotics Geography lcsh:TK5101-6720 robots Robot Computer vision Artificial intelligence business Particle filter |
| Zdroj: | FRUCT Proceedings of the XXth Conference of Open Innovations Association FRUCT, Vol 388, Iss 17, Pp 173-178 (2015) |
| DOI: | 10.1109/fruct.2015.7117989 |
| Popis: | In this paper we propose a method for solving the SLAM problem for mobile robot when moving in an unknown environment. Our method takes computational advantages of the FastSLAM algorithm. To estimate the position of the robot, we use a particle filter. The weights for the set of particles that characterize the expected position of the robot, are determined by the condition number of the plane homography matrix. It can be considered as the projective mapping of points of the scene on the two-dimensional surface of camera sensor. A set of unscented Kalman filters is used to estimate the positions of detected landmarks which are forming the map of the observed environment. Methods for detecting and description of landmarks were not considered in this paper, as it is beyond the scope of this work. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|