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Self-localization of Mobile Robot
in Unknown Environment
Alexander Prozorov
Alexander Tyukin
Ilya Lebedev
Andrew Priorov
P.G. Demidov Yaroslavl State University
17th FRUCT Conference, Yaroslavl, 20-24 April 2015
Autonomous Navigation Issue
2
Controller Robot
Measurements (camera stream)
Controls (commands)
17th FRUCT Conference, Yaroslavl, 20-24 April 2015
Simultaneous Localization and Mapping
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Given:
The robot’s controls
Observations of key features
Estimate:
Map of features
Path of the robot
Approaches:
Extended Kalman Filter
Particle Filter
Graph based
17th FRUCT Conference, Yaroslavl, 20-24 April 2015
Problem Statement
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A robot moves through xt-1…xt+1 locations with controls
ut-1...ut+1 and observes landmark m as zt-1…zt+1.
17th FRUCT Conference, Yaroslavl, 20-24 April 2015
Probabilistic Formulation of the SLAM
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Let variables x,y,z characterizes the positions of our robot v and
landmarks p0, p1, ... pm, then probability p(x,y,z | Ui, Zi) can be
represented as follows:
17th FRUCT Conference, Yaroslavl, 20-24 April 2015
State Space
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<R,T> - localization
<R,T,map> - simultaneous localization and mapping
Landmark 1 Landmark 2 Landmark M … R,T
… Landmark 1 Landmark 2 Landmark M … R,T
Landmark 1 Landmark 2 Landmark M … R,T
17th FRUCT Conference, Yaroslavl, 20-24 April 2015
Camera Model
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Interframe homography: H = RθψRφTxy = const·RφTxy
17th FRUCT Conference, Yaroslavl, 20-24 April 2015
The Malaga Urban Dataset
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Detection of Landmarks
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Unscented Kalman Filtering
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Tracking Landmarks
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Average Estimation Error
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Pose Estimation
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Initialize particles Weight particles Resample particles Prediction
17th FRUCT Conference, Yaroslavl, 20-24 April 2015
Trajectory Estimation
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Actual trajectory
FastSLAM
uFastSLAM
17th FRUCT Conference, Yaroslavl, 20-24 April 2015
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Self-localization of Mobile Robot
in Unknown Environment
Alexander Prozorov
Alexander Tyukin
Ilya Lebedev
Andrew Priorov
P.G. Demidov Yaroslavl State University
17th FRUCT Conference, Yaroslavl, 20-24 April 2015