发表机构
DGIST（大邱庆北科学技术院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MarsLab是一个开源的ROS2火星车模拟器，结合HiRISE地形与可调环境设置，在Isaac Sim中运行Perseverance级模型，支持SLAM和VPR基准测试，用于开发自主导航算法。
AI 中文摘要
未来的火星任务将需要能够在非结构化地形、变化的光照、大气尘埃和有限通信条件下运行的火星车自主性。模拟是在部署前研究这些条件的实用方法，但现有的火星相关资源在范围上有所不同，包括面向任务的模拟器、固定的模拟数据集、特定任务的环境和开放的机器人接口。在此背景下，我们提出了MarsLab，一个开源的、基于ROS2的火星车模拟器，用于自主性和导航算法的开发。MarsLab结合了HiRISE衍生的和程序化的地形，具有可定制的岩石、陨石坑、太阳光照和大气尘埃设置，并在NVIDIA Isaac Sim中运行Perseverance级别的火星车模型。运行时通过标准ROS2主题发布RGB、深度、RGB-D点云、LiDAR、IMU、轮式里程计和地面真值（GT）位姿数据。我们通过跨感知模态、尘埃水平、场景几何和路线长度的同时定位与地图构建（SLAM）基准测试，以及在光照和尘埃变化下重复火星基地穿越的视觉位置识别（VPR）基准测试来展示MarsLab。结果表明，受控的场景变化和共享的GT轨迹如何用于在同一模拟器内比较轨迹级估计和图像级位置识别。我们的项目页面：此https URL。
英文摘要
Future Mars missions will require rover autonomy that can operate across unstructured terrain, changing illumination, atmospheric dust, and limited communication. Simulation is a practical way to study these conditions before deployment, but existing Mars-relevant resources differ in scope, including mission-oriented simulators, fixed analog datasets, task-specific environments, and open robotics interfaces. In this context, we present MarsLab, an open-source, ROS2-native Mars rover simulator for autonomy and navigation algorithm development. MarsLab combines HiRISE-derived and procedural terrain with customizable rock, crater, solar-illumination, and atmospheric-dust settings, and runs a Perseverance-class rover model in NVIDIA Isaac Sim. The runtime publishes RGB, depth, RGB-D point clouds, LiDAR, IMU, wheel odometry, and Ground Truth (GT) pose data through standard ROS2 topics. We demonstrate MarsLab with Simultaneous Localization and Mapping (SLAM) benchmarks across sensing modalities, dust levels, scene geometry, and route length, and with Visual Place Recognition (VPR) benchmarks over repeated Mars Base traversals under illumination and dust changes. The results illustrate how controlled scene variation and shared GT trajectories can be used to compare trajectory-level estimation and image-level place recognition within the same simulator. Our Project Page: https://kimhoyun-robotair.github.io/MarsLab/.
Comments8 pages, 11 figures. Accepted for publication at the 2026 International Conference on Space Robotics (iSpaRo)
