StairNet

Computer vision can be used in robotic exoskeleton control to improve transitions between locomotion modes through the prediction of future environmental states. We developed StairNet to focus specifically on stair recognition due to the potential safety implications and the theoretical risk of injury resulting from environmental misclassification during stair ascent. The dataset was developed using the “ExoNet” database – the largest and most diverse open-source dataset of wearable camera images of walking environments. StairNet contains 515,452 labelled images from six of the twelve original ExoNet classes. These images were carefully reclassified into four classes which use novel definitions created from a computer vision perspective with the goal of increasing the accuracy of the cutoff points between classes within the dataset. Additionally, the dataset was manually parsed numerous times during annotation to reduce misclassification errors and remove images with large abstractions from the dataset.

计算机视觉可应用于机器人外骨骼（robotic exoskeleton）控制领域，通过预测未来环境状态以提升运动模式间的切换性能。鉴于上楼过程中环境分类错误可能引发安全隐患，且存在导致使用者受伤的理论风险，我们专门开发了StairNet以专注于楼梯识别任务。本数据集基于"ExoNet"数据库构建——该数据库是目前规模最大、场景覆盖最全面的开源可穿戴摄像头步行环境图像数据集。StairNet数据集包含源自原始"ExoNet" 12个分类中6个分类的515,452张标注图像。我们将这些图像按照计算机视觉视角下的全新分类定义重新划分为4个类别，旨在提升数据集内各类别间分界点的分类准确性。此外，在标注阶段，我们对数据集开展了多轮人工核查与解析，以减少分类错误，并移除了存在过度抽象问题的图像。

搜集汇总

背景与挑战

背景概述

StairNet是一个专注于楼梯识别的计算机视觉数据集，旨在提升机器人外骨骼在楼梯上升时的控制安全性，避免因环境误分类导致的伤害风险。该数据集基于ExoNet数据库构建，包含515,452张图像，这些图像从原始类别中重新分类为4个新类别，以提高类别间界限的准确性，并在标注过程中经过多次手动解析以确保质量。