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RGB-D-Based Stair Detection and Estimation Using Deep Learning.

Chen Wang¹, Zhongcai Pei¹, Shuang Qiu¹

¹School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.

Sensors (Basel, Switzerland)

|February 28, 2023

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This study introduces a new stair detection network for robots using both color and depth information. The multimodal approach significantly improves accuracy and speed for reliable stair navigation in various conditions.

Area of Science:

Robotics and Computer Vision
Environmental Perception for Autonomous Systems

Background:

Stair detection is crucial for mobile robot navigation in buildings.
Existing methods struggle with multimodal sensor fusion and challenging visual conditions like low light or blur.

Purpose of the Study:

To develop a robust stair detection network for autonomous mobile robots.
To enhance stair detection reliability using complementary RGB and depth data.

Main Methods:

Proposed a novel stair detection network integrating red-green-blue (RGB) and depth inputs.
Introduced a selective module for effective feature fusion between RGB and depth data.
Developed postprocessing algorithms for accurate stair geometric parameter estimation.

Keywords:

RGB-D deep learning multimodality stair detection stair geometric parameter estimation

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Main Results:

Achieved superior performance compared to state-of-the-art deep learning methods.
Improved accuracy by 5.64%, recall by 7.97%, and reduced runtime by 3.81 ms.
Estimated stair geometric parameters with root mean square errors within 15 mm (ascending) and 25 mm (descending).

Conclusions:

The multimodal approach and selective module effectively improve stair detection.
The proposed method offers fast and reliable stair detection suitable for real-time robotic applications.