移动机器人可以在确保个人安全下有效减少人类的劳动行为,已经广泛应用到运输、救援、勘探、工业自动化等领域.针对特种越障机器人在城镇和工厂等以平面为主的非结构化场景下作业,提出一种依靠视觉引导的图像处理与障碍高度评估新方法.在图像预处理流程中,为了更好地保留特定朝向的图像边缘,在双边滤波算法的基础上,引入了一种呈高斯分布的斜卷积空域卷积核,改进为斜卷积双边滤波,并与其他边缘保持滤波算法进行比较.实验结果表明:不同算法信噪比一致时,斜卷积双边滤波算法的PFOM(pratt's figure of merit)均值高于引导滤波与加权最小二乘滤波;当σ(标准差)=0.02 时,斜卷积双边滤波的PFOM比引导滤波高出12.18%,比加权最小二乘滤波高4.4%.对具有普遍性的台阶沿向与沿向垂直使用斜卷积双边滤波处理后提取边缘,越障机器人在不同光照条件下进行不同高度台阶越障实验.实验表明:机器人在视觉系统引导下可快速正对台阶,越障成功率为100%.
Improvement and optimization of special robot vision guidance algorithm based on oblique convolution bilateral filtering
Mobile robots can effectively reduce human labor behavior while ensuring personal safety.Robots have been widely used in transportation,rescue,industrial automation and other fields.In this paper,a visual guided image processing and obstacle height evaluation method was proposed for special obstacle jumping robots to operate in unstructured scenes mainly in planes such as towns and factories.In the process of image preprocessing,in order to better preserve the image edges with specific orientation,a skew convolution spatial convolution kernel with Gaussian distribution was introduced on the basis of the two-sided filtering algorithm,which was improved to skew convolution two-sided filtering,and compared with other edge preserving filtering algorithms.The experimental results show that when the SNR of different algorithms is consistent,the average PFOM of oblique convolution bilateral filtering algorithm was higher than that of guided filtering and weighted least squares filtering.When σ =0.02,the PFOM of oblique convolution bilateral filtering is 12.18%higher than that of guided filtering and 4.4%higher than that of weighted least squares filtering.The obstacle crossing robot was directly aligned with the step at a height close to 100 mm,150 mm and 200 mm under different lighting conditions,and the success rate of obstacle crossing was 100%under the guidance of the visual system.
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