It has been proved that, based on planar region alignment, camera ego-motion motion can be robustly estimated with a multistage approach in [24], Based on this observation, a visual regulation approach based on planar...
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ISBN:
(纸本)9781467312752
It has been proved that, based on planar region alignment, camera ego-motion motion can be robustly estimated with a multistage approach in [24], Based on this observation, a visual regulation approach based on planar region alignment is proposed. This paper is composed of two parts. In the first part, the image motion model of planar point is reviewed first, which will be used for camera motion estimation. Then a simple least square method for computing homography matrix H is introduced. The basic procedure of multistage motion estimation based on planar region alignment is summarized. In the second part, the kinematic model of monocular camera based mobile robot system is deduced with a constrained camera-robot configuration, the visual regulation control law is derived and the stability of the close loop system is analyzed in the sense of Lyapunov stability theory. The motion estimation experiment results are presented with real images, and simulation results show the convergence of the proposed visual regulation.
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Simultaneous localization and mapping (SLAM) is a key technology for mobile robots operating under unknown environment. While FastSLAM algorithm is a popular solution to the SLAM problem, it suffers from two major d...
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Simultaneous localization and mapping (SLAM) is a key technology for mobile robots operating under unknown environment. While FastSLAM algorithm is a popular solution to the SLAM problem, it suffers from two major drawbacks: one is particle set degeneracy due to lack of observation information in proposal distribution design of the particle filter; the other is errors accumulation caused by linearization of the nonlinear robot motion model and the nonlinear environment observation model. For the purpose of overcoming the above problems, a new iterated sigma point FastSLAM (ISP-FastSLAM) algorithm is proposed. The main contribution of the algorithm lies in the utilization of iterated sigma point Kalman filter (ISPKF), which minimizes statistical linearization error through Gaussian-Newton iteration, to design an optimal proposal distribution of the particle filter and to estimate the environment landmarks. On the basis of Rao-Blackwellized particle filter, the proposed ISP-FastSLAM algorithm is comprised by two main parts: in the first part, an iterated sigma point particle filter (ISPPF) to localize the robot is proposed, in which the proposal distribution is accurately estimated by the ISPKF; in the second part, a set of ISPKFs is used to estimate the environment landmarks. The simulation test of the proposed ISP-FastSLAM algorithm compared with FastSLAM2.0 algorithm and Unscented FastSLAM algorithm is carried out, and the performances of the three algorithms are compared. The simulation and comparing results show that the proposed ISP-FastSLAM outperforms other two algorithms both in accuracy and in robustness. The proposed algorithm provides reference for the optimization research of FastSLAM algorithm.
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