This paper improves the accuracy of a mine robot’s positioning and mapping for rapid rescue. Specifically, we improved the FastSLAM algorithm inspired by the lion swarm optimization method. Through the division of labor between different individuals in the lion swarm optimization algorithm, the optimized particle set distribution after importance sampling in the FastSLAM algorithm is realized. The particles are distributed in a high likelihood area, thereby solving the problem of particle weight degradation. Meanwhile, the diversity of particles is increased since the foraging methods between individuals in the lion swarm algorithm are different so that improving the accuracy of the robot’s positioning and mapping. The experimental results confirmed the improvement of the algorithm and the accuracy of the robot.
Due to the good invariance of scale, rotation, illumination, SIFT (Scale Invariant Feature Transform) descriptor is commonly used in image matching. but its algorithm is complicated and computation time is long. To improve SIFT feature matching algorithm efficiency, the method of reducing similar measure matching cost is mentioned. Euclidean distance is replaced by the linearcombination of cityblock distance and chessboard distance, and reduce character point in calculating with results of part feature. The experimental results show that the algorithm can reduce the rate of time complexity and maintain robust quality at the same time, image matching efficiency is improved.
The mobile robot is moved by receiving instructions through wireless communication, and the particle filter is used to simultaneous localization and mapping. Aiming at the problem of the degradation of particle filter weights and loss of particle diversity, which leads to the decrease of filter accuracy, this paper uses the plant cell swarm algorithm to optimize the particle filter. First of all, combining the characteristics of plant cells that affect the growth rate of cells when the auxin content changes due to light stimulation realizes the optimization of the particles after importance sampling, so that they are concentrated in the high-likelihood area, and the problem of particle weight degradation is solved. Secondly, in the process of optimizing particle distribution, the auxin content of each particle is different, which makes the optimization effect on each particle different, so it effectively solves the problem of particle diversity loss. Finally, a simulation experiment is carried out. During the experiment, the robot moves by receiving control commands through wireless communication. The experimental results show that the algorithm effectively solves the problem of particle weight degradation and particle diversity loss and improves the filtering accuracy. The improved algorithm is verified in the simultaneous localization and mapping of the robot, which effectively improves the robot’s performance at the same time positioning accuracy. Compared with the classic algorithm, the robot positioning accuracy is increased by 49.2%. Moreover, the operational stability of the algorithm has also been improved after the improvement.
This paper presents an approach to binocular vision simultaneous localization and mapping (SLAM). SIFT (Scale Invariant Feature Transform) algorithm is used to extract the Natural landmarks. But SIFT algorithm is complicated and computation time is long. Firstly, the linear combination of cityblock distance and chessboard distance is comparability measurement; secondly, partial features are used to matching. SLAM is completed by fusing the information of SIFT features and robot information with EKF. Mahalanobisis distance is used in data association which solve the problem that the scale of data association increase with the map grows in process of SLAM .The simulation experiment indicate that the proposed method reduce computational complexity, and with high localization precision in indoor environments. Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/27/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Proc. of SPIE Vol. 7820 78200T-2 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/27/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx
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