maum-ai/CostNav-Teleop-Dataset

CostNav Teleop Dataset是一个大规模的人类远程操作记录集合，用于机器人导航在城市人行道模拟环境中的性能评估。该数据集作为[CostNav基准](https://github.com/worv-ai/CostNav)的一部分收集，该基准使用真实世界的经济成本和收入指标而非纯技术指标来评估导航系统。数据集包含2,203个远程操作片段，总计50.2小时的驾驶数据，由4名人类操作员使用操纵杆控制Segway E1送货机器人在NVIDIA Isaac Sim中收集。每个片段以ROS 2 bag文件形式存储在MCAP格式中，包含同步的多模态传感器数据。

The **CostNav Teleop Dataset** is a large-scale collection of human teleoperation recordings for robot navigation in an urban sidewalk simulation environment. It was collected as part of the [CostNav benchmark](https://github.com/worv-ai/CostNav), which evaluates navigation systems using real-world economic cost and revenue metrics rather than purely technical metrics. The dataset contains **2,203 teleoperation episodes** totaling **50.2 hours** of driving data, collected by 4 human operators using joystick control of a **Segway E1 delivery robot** in NVIDIA Isaac Sim. Each episode is stored as a ROS 2 bag file in MCAP format with synchronized multi-modal sensor data.