A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots
A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots
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DOI：
10.1109/lra.2015.2509024
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发表时间：
2016-07-01
影响因子：
5.2
通讯作者：
Gambardella, Luca M.
中科院分区：
文献类型：
--
作者：
Giusti, Alessandro;Guzzi, Jerome;Gambardella, Luca M.
我们研究的问题，从单一的单眼图像感知森林或山间的路径，从一个机器人的视点在路径本身。先前的文献主要关注轨迹分割，使用图像显著性或外观对比度等底层特征；我们提出了一种基于深度神经网络作为监督图像分类器的不同方法。通过一次对整个图像进行操作，我们的系统输出与观测方向相比的轨迹主方向。在大型真实世界数据集（我们提供下载）上计算的定性和定量结果表明，我们的方法优于其他方法，并且在相同的图像分类任务上测试的准确性可与人类的准确性相媲美。本文报道了利用该信息进行四旋翼飞行器隐形尾迹控制的初步结果。据我们所知，这是描述感知森林试验方法的第一封信，这是在四旋翼微型飞行器上演示的。
We study the problem of perceiving forest or mountain trails from a single monocular image acquired from the viewpoint of a robot traveling on the trail itself. Previous literature focused on trail segmentation, and used low-level features such as image saliency or appearance contrast; we propose a different approach based on a deep neural network used as a supervised image classifier. By operating on the whole image at once, our system outputs the main direction of the trail compared to the viewing direction. Qualitative and quantitative results computed on a large real-world dataset (which we provide for download) show that our approach outperforms alternatives, and yields an accuracy comparable to the accuracy of humans that are tested on the same image classification task. Preliminary results on using this information for quadrotor control in unseen trails are reported. To the best of our knowledge, this is the first letter that describes an approach to perceive forest trials, which is demonstrated on a quadrotor micro aerial vehicle.