MarsLab: A Martian Rover Simulator for Planetary Rover Autonomous Navigation

2026-09-28

AI Summary

This study addresses the fragmentation of existing Mars simulation resources and their limited support for developing autonomous navigation algorithms in complex environments by constructing the first open-source, ROS2-native Mars rover simulator. The system integrates HiRISE-derived terrain, dynamic illumination, and atmospheric dust models to enable standardized multi-sensor data publishing and trajectory-level evaluation. Leveraging NVIDIA Isaac Sim and procedural generation techniques, this work conducts benchmarking for SLAM and visual localization, validating comparative navigation performance across diverse perception modalities and environmental perturbations. Results demonstrate that the proposed simulator exhibits high fidelity and versatility, providing a unified open-source platform for advancing planetary exploration algorithm research.

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/.

Problem

• Mars rover simulator
• Autonomous navigation
• Simulation environment
• Planetary exploration

Innovation

• Mars Rover Simulator
• ROS2
• SLAM Benchmarking
• Visual Place Recognition
• Isaac Sim