Deng Fucheng, Zhu Xiaorui, He Chao
Harbin Institute of Technology (Shenzhen), Shenzhen 518055, Guangdong, China.
Sensors (Basel). 2017 Sep 13;17(9):2101. doi: 10.3390/s17092101.
Environment perception is essential for autonomous mobile robots in human-robot coexisting outdoor environments. One of the important tasks for such intelligent robots is to autonomously detect the traversable region in an unstructured 3D real world. The main drawback of most existing methods is that of high computational complexity. Hence, this paper proposes a binocular vision-based, real-time solution for detecting traversable region in the outdoors. In the proposed method, an appearance model based on multivariate Gaussian is quickly constructed from a sample region in the left image adaptively determined by the vanishing point and dominant borders. Then, a fast, self-supervised segmentation scheme is proposed to classify the traversable and non-traversable regions. The proposed method is evaluated on public datasets as well as a real mobile robot. Implementation on the mobile robot has shown its ability in the real-time navigation applications.
在人机共存的户外环境中，环境感知对于自主移动机器人至关重要。此类智能机器人的一项重要任务是在非结构化的三维现实世界中自主检测可通行区域。大多数现有方法的主要缺点是计算复杂度高。因此，本文提出了一种基于双目视觉的实时解决方案，用于检测户外的可通行区域。在所提出的方法中，基于多元高斯的外观模型由通过灭点和主导边界自适应确定的左图像中的样本区域快速构建。然后，提出了一种快速的自监督分割方案，对可通行区域和不可通行区域进行分类。所提出的方法在公共数据集以及实际移动机器人上进行了评估。在移动机器人上的实现展示了其在实时导航应用中的能力。
