基于激光雷达与视觉融合的水面漂浮物检测

Detection of Floating Objects on Water Surface Based on Fusion of Lidar and Vision

针对水面清污机器人对漂浮目标的检测问题,提出了一种将三维激光雷达点云数据与视觉信息融合检测的方法。首先,视觉识别部分采用CornerNet-Lite目标检测网络,通过对大量样本的训练实现水面漂浮物的检测,得到候选目标的种类和置信度。然后,通过相机和激光雷达的标定将激光雷达三维点云数据投影到二维像素平面,并根据相对像素面积大小的概念定义了激光雷达检测目标物的置信度。最后,调整激光雷达和相机检测目标置信度权重比例,构成新的判定置信度,并通过比较判定置信度和设定阈值的大小来判断是否检测到目标。试验结果表明,该方法比单独使用CornerNet-Lite算法的检测准确率更高,消除了水面倒影和波纹的影响,降低了虚警率。

Aiming at the problem of detection of floating targets by water surface cleaning robots, a method of fusion detection of 3 D lidar point cloud data and visual information is proposed. First, the visual recognition part adopts CornerNet-Lite target detection network, through the training of a large number of samples to achieve the detection of floating objects on the water surface, and obtain the type and confidence of candidate targets. Then, through the calibration of the camera and lidar, the lidar three-dimensional point cloud data is projected onto the two-dimensional pixel plane, and the confidence of the lidar detection target is defined according to the concept of relative area size. Finally, adjust the confidence weight ratio of lidar and camera detection targets to form a new decision function, and determine whether the target is detected by comparing the size of the decision function with the set threshold. Experimental results show that this method has higher accuracy than CornerNet-Lite algorithm alone, eliminates the effects of water reflection and ripples, and reduces the false alarm rate.