MarsLab simulator enables testing Mars rover navigation under harsh conditions
MarsLab: A Martian Rover Simulator for Planetary Rover Autonomous Navigation
Robotics
Summary
Mars rovers need to navigate tricky Martian landscapes with dust and changing light while communicating slowly with Earth. The authors created MarsLab, a simulator that mimics these challenges using detailed Mars terrain and a rover model. It helps test how well rover software can map and recognize places on Mars using different sensors and environmental effects. This way, rover developers can improve navigation before sending real rovers to Mars.
What this means in practice
- •For planetary rover developers: Test and compare rover navigation algorithms in realistic Mars-like environments considering dust and lighting variations.
- •For robotics simulation engineers: Integrate MarsLab with existing robotics tools via ROS2 to build and validate planetary exploration software.
Authors
Hoyun Kim, Beomsu Kim, Giseop Kim
Abstract
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/.