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- W2393290700 abstract "Localization is a fundamental requirement for a mobile robot.In indoor environments,the objects are polygonal usually.These objects can be described as line segments.The laser sensor can obtain the information of the environment automatically.By making use of the Hough transform technology,laser sensor data can be divided into different clusters.Then the least-square method is used to extract features of line segments.The odometry model and the observation model are established.In the Extended Kalman Filter(EKF) algorithm,a nonlinear system is linearized to a linear system,which may lead to the decline of accuracy and even instability of the filter,and it is hard to calculate Jacobian matrices.In order to overcome the disvantages of the EKF,the Unscented Kalman Filer(UKF) is used to integrate the data from odometry and the laser sensor.By using this method,the accurate pose of the mobile robot can be obtained without linearizing the system model.It is proved by experiments that the algorithm based on the UKF is obviously more accurate than the algorithm based on the EKF." @default.
- W2393290700 created "2016-06-24" @default.
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- W2393290700 date "2009-01-01" @default.
- W2393290700 modified "2023-09-23" @default.
- W2393290700 title "SLAM of indoor mobile robots based on the Unscented Kalman Filter" @default.
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