StairNetV3: Depth-aware Stair Modeling using Deep Learning

TL;DR

This paper introduces a depth-aware monocular vision method for accurate stair modeling in autonomous robots, combining CNN-based geometric feature extraction with depth information for improved reconstruction and real-time performance.

Contribution

It proposes a novel joint CNN framework for stair geometric features and depth prediction, enhancing monocular stair modeling accuracy and speed.

Findings

Achieved a 3.4% increase in IOU over previous methods.

Developed a lightweight model suitable for real-time applications.

Provided a new dataset for stair perception research.

Abstract

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.