This paper introduces DogSurf - a newapproach of using quadruped robots to help visually impaired people navigate in real world. The presented method allows the quadruped robot to detect slippery surfaces, and to use audio and haptic feedback to inform the user when to stop. A state-of-the-art GRU-based neural network architecture with mean accuracy of 99.925% was proposed for the task of multiclass surface classification for quadruped robots. A dataset was collected on a Unitree Go1 Edu robot. The dataset and code have been posted to the public domain.

翻译：本文介绍了DogSurf——一种利用四足机器人帮助视障人士在真实环境中导航的新方法。该方法使四足机器人能够检测湿滑地面，并通过音频和触觉反馈提醒用户何时停止。针对四足机器人多类别地面分类任务，本文提出了一种基于GRU的先进神经网络架构，平均准确率达到99.925%。数据集通过Unitree Go1 Edu机器人采集完成。该数据集及代码已公开发布至公共领域。