SJTU-ViSYS/M2DGR resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1194 stars · 130 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

M2DGR is a multi-modal, multi-scenario SLAM dataset collected with ground robots, providing synchronized vision, lidar, IMU, GNSS, event, and thermal-infrared data across indoor and outdoor environments. It serves as a benchmark for evaluating state-of-the-art SLAM and localization algorithms.

Use cases

• benchmark a new SLAM algorithm against LIO-SAM and ORB-SLAM3
• download multi-sensor robot datasets for lidar-inertial-visual odometry research
• evaluate GNSS-degraded indoor localization with lidar and IMU data
• test event camera or thermal-infrared SLAM methods
• find challenging sequences with lifts, streets, and halls for robot navigation
• compare ground robot localization systems on a public benchmark

When to choose

• you need real-world multi-modal sensor data for ground robot SLAM research
• you want a recognized benchmark to validate a newly proposed SLAM system
• you need sequences with degenerate motion or GNSS-denied scenarios

When to avoid

• you need aerial or underwater robot data
• you want a ready-to-run SLAM implementation rather than raw sensor data
• you need synthetic or simulated environments

Facets

dataset · maturity stable

simulation benchmarking computer-vision robotics autonomous-vehicles computer-vision cross-platform slam multi-modal lidar imu gnss ground-robots benchmark localization mapping algorithms linux ros

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