论文标题
基于RGB-D的楼梯检测使用深度学习进行自主楼梯攀爬
RGB-D-based Stair Detection using Deep Learning for Autonomous Stair Climbing
论文作者
论文摘要
楼梯是城市环境中常见的建筑结构，楼梯检测是自动移动机器人环境感知的重要组成部分。大多数现有算法都难以将双眼传感器的视觉信息有效地组合在一起，并确保在夜间和在极其模糊的视觉线索中可靠的检测。为了解决这些问题，我们提出了具有RGB和深度图输入的神经网络体系结构。具体来说，我们设计了一个选择性模块，该模块可以使网络学习RGB地图和深度图之间的互补关系，并有效地结合了RGB地图中的信息以及不同场景中的深度图。此外，我们设计了一个线聚类算法，用于检测结果的后处理，该算法可以充分利用检测结果以获得几何阶梯参数。与现有的最新深度学习方法相比，我们的数据集中的实验表明，我们的方法可以实现更好的准确性和回忆，分别为5.64％和7.97％，我们的方法也具有极快的检测速度。轻量级版本可以通过相同的分辨率每秒实现300 +帧，这可以满足大多数实时检测场景的需求。
Stairs are common building structures in urban environments, and stair detection is an important part of environment perception for autonomous mobile robots. Most existing algorithms have difficulty combining the visual information from binocular sensors effectively and ensuring reliable detection at night and in the case of extremely fuzzy visual clues. To solve these problems, we propose a neural network architecture with RGB and depth map inputs. Specifically, we design a selective module, which can make the network learn the complementary relationship between the RGB map and the depth map and effectively combine the information from the RGB map and the depth map in different scenes. In addition, we design a line clustering algorithm for the postprocessing of detection results, which can make full use of the detection results to obtain the geometric stair parameters. Experiments on our dataset show that our method can achieve better accuracy and recall compared with existing state-of-the-art deep learning methods, which are 5.64% and 7.97%, respectively, and our method also has extremely fast detection speed. A lightweight version can achieve 300 + frames per second with the same resolution, which can meet the needs of most real-time detection scenes.
