M2DGR: A Multi-Sensor and Multi-Scenario SLAM Dataset for Ground Robots

We introduce M2DGR: a novel large-scale dataset collected by a ground robot with a full sensor-suite including six fish-eye and one sky-pointing RGB cameras, an infrared camera, an event camera, a Visual-Inertial Sensor (VI-sensor), an inertial measurement unit (IMU), a LiDAR, a consumer-grade Globa...

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Vydáno v:IEEE robotics and automation letters Ročník 7; číslo 2; s. 2266 - 2273
Hlavní autoři: Yin, Jie, Li, Ang, Li, Tao, Yu, Wenxian, Zou, Danping
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Cameras
data sets for robotic vision
Data sets for SLAM
Datasets
Global navigation satellite system
Indoor environments
Inertial platforms
Inertial sensing devices
Infrared cameras
Laser radar
Motion capture
Navigation systems
Robot vision systems
Robots
Sensors
Simultaneous localization and mapping
State-of-the-art reviews
Visualization
ISSN:2377-3766, 2377-3766
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Popis
Shrnutí:We introduce M2DGR: a novel large-scale dataset collected by a ground robot with a full sensor-suite including six fish-eye and one sky-pointing RGB cameras, an infrared camera, an event camera, a Visual-Inertial Sensor (VI-sensor), an inertial measurement unit (IMU), a LiDAR, a consumer-grade Global Navigation Satellite System (GNSS) receiver and a GNSS-IMU navigation system with real-time kinematic (RTK) signals. All those sensors were well-calibrated and synchronized, and their data were recorded simultaneously. The ground truth trajectories were obtained by the motion capture device, a laser 3D tracker, and an RTK receiver. The dataset comprises 36 sequences (about 1 TB) captured in diverse scenarios including both indoor and outdoor environments. We evaluate state-of-the-art SLAM algorithms on M2DGR. Results show that existing solutions perform poorly in some scenarios. For the benefit of the research community, we make the dataset and tools public. The webpage of our project is https://github.com/SJTU-ViSYS/M2DGR .
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2021.3138527