摘要
无人机在灾后煤矿救灾方面的应用越来越广泛,但是由于井下环境复杂多变,发生灾害时更加无法准确估计井下环境,因此需要无人机在工作时精确定位其所在位置,为井上操控以及救灾提供及时有效的信息。该文研究了两种经典的定位算法:基于EKF的SLAM定位算法和基于Rao-Blackwellized粒子滤波器的FastSLAM定位算法,对两种算法进行了数学推导分析,建立了算法数学模型,得到非线性方程,使用Matlab仿真后发现基于Rao-Blackwellized粒子滤波器的FastSLAM定位算法具有更好的性能,可以快速准确地定位无人机的位置姿态,鲁棒性和算法独立性较好,同时运行时间短,具有更高的定位精度。
UAVs are more and more used in disaster coal mine widely.However,due to the complicated and changeable environment,it is even more difficult to estimate the underground stituation in the event of disasters accurately.Therefore,UAVs need to locate their positions surely to provide information for work control and disaster relief timely and effectivty.In this paper,we study two classical localization algorithms：SLAM localization algorithm based on EKF and SLAM localization algorithm based on Rao-Blackwellized particle filter.The two algorithms are deduced and analyzed mathematically,and the mathematical model is established,then get the nonlinear equations.After simulated by Matlab,we find that the SLAM algorithm based on Rao-Blac- kwellized particle filter has better performance,can locate the position and attitude of the UAV quickly and accurately,and has better robustness and independence,while the running time short,with a higher positioning accuracy.
出处
《电子质量》
2017年第12期56-61,66,共7页
Electronics Quality
