复杂地面车辙识别试验与验证

Test and verification of rut recognition on complex ground

火星表面地形地貌复杂,为了保证火星车行驶安全,需要对巡视器周边土壤的图像信息进行判别和分类。首先,根据试验场地和图像信息等对图像进行预处理,建立鸟瞰图像。接着,以鸟瞰图像为基础建立图像块并建立数据集,建模集和预测集分别包含315组和135组数据。然后,在划分的数据集基础上建立神经网络模型,并对数据进行训练和分类。最后,根据得到的分类模型对图像进行分类,得到感兴趣区域。分类结果表明:应用ResNet50得到的模型其建模集和预测集的分类准确率分别为75.56%和81.48%。该方法可实现巡视器周边地表类型的分类,并提取图像的感兴趣区域,以便实现更为精准的判别,可用于实现火星车通过性感知、风险预测和路径规划,为未来智能星球车移动系统研制和探测提供理论和技术支持。

The geomorphological characterization of Mars is complex.Therefore,to ensure the safe driving of rover,it is essential to understand the surface state around the rover through images captured by onboard digital cameras.The images are first preprocessed using stereo vision to create an aerial view,which is then divided into equal-sized blocks.Next,calibration and prediction datasets are created,containing 315 and 135 datapoints,respectively.Based on these datasets,a neural network model is developed.Finally,the image is classified using the resulting classification model to identify the region of interest.The results of the classification show that its accuracy on the calibration and prediction datasets using ResNet50 is 75.56%and 81.48%,respectively.This method can help researchers characterize the surface types around UGVs and identify the regions of interest that may provide more valuable information from the images.It can also be used for traversability prediction,risk assessment,and automatic path planning.