基于图像技术与粒子滤波融合新算法的机器人多目标跟踪

摘要

The robot target tracking technology is an important part of robot navigation and positioning tech-nology. In order to accurately track the robot's position and speed information. This paper proposes a fusion method of particle filter algorithm . The algorithm use the image color histogram, combined with the feature of Gaussian kernel function mining data, it also fusion particle filter algorithm to automatic target tracking robot. In addition, for the sample impoverishment, namely, the situation that the majority of particles over-lap on one single point in the computation of particle filter, resampling is utilitied , but anisotropies of some particles are prone to lose during this process, which may leads to low tracing precision, or even failure of trace, so a new binding method of resampling, on the basis of standard filter, is put forward . The experi-mental results show that the improved particle filter algorithm based on color distribution can effectively re-duce the sample differentiation problem, and can be high precision identify robot move, turn and meet goals.%机器人的目标跟踪技术是机器人定位导航技术中的重要一环.为了能够精确的跟踪机器人的位置和速度信息,文章提出了一种多方法融合的粒子滤波算法.该算法是采用图像颜色直方图结合高斯核函数挖掘特征数据,融合粒子滤波改进算法自动追踪机器人目标.此外,为了解决粒子滤波中样本贫化,即在粒子滤波计算中很大一部分粒子重叠到一个单独的点上的情况,需要重采样计算解决此问题,但在重采样过程中容易造成一些粒子丢失各向异性导致跟踪精度降低,甚至跟踪目标失败,结合标准粒子滤波提出了一种新型重采样约束方法.实验结果表明,基于颜色分布改善后的粒子滤波算法能有效的减少样本分化问题,并且可以高精度的识别出移动、急转和相遇的机器人目标.