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A Vision-Based Road Surface Slope Estimation Algorithm for Mobile Service Robots in Indoor Environments

机译:室内环境下基于视觉的移动服务机器人路面坡度估计算法

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It is vital for mobile robots to maintain safe and stable operation in environments with slopes, e.g. airports and shopping malls. Different slopes correspond to different accelerations of robots, and the robot also require different driving forces or braking forces to go up or down different slopes. If the robot does not have the ability to recognize of the slope, it cannot generate suitable commands according to the change of slope. That is to say, when going uphill, the lack of driving force may cause the robot to stagnate on the slope; when downhill, insufficient braking force may cause the robot to lose control. To solve such problems, a slope estimation algorithm is proposed in this paper based on RGB-D camera. Firstly, we use the RGB-D camera to obtain the information of the road surface. Secondly, the edge of the slope is detected by Canny Edge Detection Algorithm. Finally, the slope is estimated based on the mathematical geometry. Experimental studies are carried out to evaluate the proposed slope estimation algorithm. Experimental results show that the slope of the road can be obtained in real time and the average slope estimation error is less than 5°.
机译:对于移动机器人来说,在坡度较大的环境(例如坡度)中保持安全稳定的运行至关重要。机场和大型购物中心。不同的坡度对应于机器人的不同加速度,并且机器人还需要不同的驱动力或制动力来使不同的坡度上升或下降。如果机器人没有识别坡度的能力,则无法根据坡度的变化生成合适的命令。也就是说,上山时,缺乏驱动力可能会导致机器人停滞在斜坡上。下坡时,制动力不足可能会导致机器人失去控制。为了解决这些问题,本文提出了一种基于RGB-D摄像机的斜率估计算法。首先,我们使用RGB-D相机获取路面信息。其次,利用Canny边缘检测算法检测出斜坡的边缘。最后,基于数学几何来估计斜率。进行了实验研究,以评估所提出的斜率估计算法。实验结果表明,该道路坡度可以实时获得,平均坡度估计误差小于5°。

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