StairNetV3: Depth-aware Stair Modeling using Deep Learning
Chen Wang Zhongcai Pei Shuang Qiu Yachun Wang Zhiyong Tang
Vision-based stair perception can help autonomous mobile robots deal with the
challenge of climbing stairs, especially in unfamiliar environments. To address
the problem that current monocular vision methods are difficult to model stairs
accurately without depth information, this paper proposes a depth-aware stair
modeling method for monocular vision. Specifically, we take the extraction of
stair geometric features and the prediction of depth images as joint tasks in a
convolutional neural network (CNN), with the designed information propagation
architecture, we can achieve effective supervision for stair geometric feature
learning by depth information. In addition, to complete the stair modeling, we
take the convex lines, concave lines, tread surfaces and riser surfaces as
stair geometric features and apply Gaussian kernels to enable the network to
predict contextual information within the stair lines. Combined with the depth
information obtained by depth sensors, we propose a stair point cloud
reconstruction method that can quickly get point clouds belonging to the stair
step surfaces. Experiments on our dataset show that our method has a
significant improvement over the previous best monocular vision method, with an
intersection over union (IOU) increase of 3.4 %, and the lightweight version
has a fast detection speed and can meet the requirements of most real-time
applications. Our dataset is available at
https://data.mendeley.com/datasets/6kffmjt7g2/1.