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RGB-Based Staircase Detection for Quadrupedal Robots: Implementation and Analysis.

Piotr Wozniak¹, Paweł Penar², Damian Bielecki²

¹Department of Computer and Control Engineering, Rzeszow University of Technology, 35-959 Rzeszow, Poland.

Sensors (Basel, Switzerland)

|December 11, 2025

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This study introduces a real-time stair detection algorithm for quadruped robots, enhancing safety by identifying hazards. The deep learning approach adapts to various camera views, enabling safer robot navigation.

Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Quadruped robots require advanced perception for safe navigation in complex environments.
Stair detection is crucial for preventing falls and damage during robot operation.
Existing methods often lack adaptability to diverse viewpoints and real-time constraints.

Purpose of the Study:

To develop and validate a robust stair detection algorithm for quadruped robots.
To enable real-time hazard detection, specifically stair proximity, for enhanced robot safety.
To create a versatile algorithm adaptable to different camera perspectives and platforms.

Main Methods:

Utilized a deep neural network (YOLO version 11n) with RGB inputs for object detection.
Trained the network on a dataset of over 26,000 images from a quadruped robot's dual cameras.

Keywords:

YOLO deep learning mobile robot object detection quadrupedal robot stair detection

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Incorporated multi-camera image training (front and bottom views) and tested on various embedded platforms.

Main Results:

Achieved an average mAP@50 of 51.30 for stair-only images and 87.56 for all images across 17 unseen sequences.
Demonstrated the algorithm's adaptability from a single training sequence to multiple diverse sequences.
Verified performance on the Unitree Go1 robot and other embedded systems.

Conclusions:

The proposed stair detection algorithm effectively enhances safety for quadruped robots navigating stairs.
The method offers real-time hazard identification and adaptability to varying environmental and camera conditions.
This research advances stair detection capabilities for legged robots, addressing real-world deployment challenges.