视觉与二维激光雷达的目标检测方法

为了改进单一传感器对目标物体的检测范围小、检测特征少以及检测准确率较低的问题，提出一种视觉与二维激光雷达的目标检测方法。在视觉检测方面提出一种改进的GoogLeNet算法实现视觉对目标物体的识别，该方法相比GoogLeNet算法在对6种目标物体的识别准确率上提高了0.7%。在二维激光雷达检测方面采用欧氏聚类算法对二维激光雷达的点云数据聚类，接着使用RANSAC算法对聚类簇中的数据点进行筛选，最后使用卡尔曼滤波算法对目标物体的位置进行预测，实现二维激光雷达在特定平面上360°对目标物体进行跟踪检测和定位。实验结果表明，该方法使得移动机器人扩大了检测范围、增加了检测特征并提高了识别准确率。

Target detection method for visual and 2D laser radar

In order to improve that the detection range of a single sensor is small, the detection features are few and the detection accuracy is low, a target detection method for visual and 2D laser radar is proposed. In terms of visual detection, an improved GoogLeNet algorithm is proposed to realize the visual recognition of target objects. Compared with GoogLeNet algorithm, this method has improved the recognition accuracy of 6 target objects by 0. 7%. In 2D laser radar detection using European clustering algorithm for 2D laser radar point cloud data clustering, and then use RANSAC algorithm for clustering data point in the cluster to filter, finally using Kalman filter algorithm to estimate the location of the target object, and realize the 2D laser radar in a particular plane 360° to detect and locate the target object tracking. Experimental results show that this method can enlarge the detection range, increase the detection features and improve the recognition accuracy of mobile robot.