DIDLM: A SLAM Dataset for Difficult Scenarios Featuring Infrared, Depth Cameras, LIDAR, 4D Radar, and Others under Adverse Weather, Low Light Conditions, and Rough Roads
Summary: arXiv:2404.09622v3 Announce Type: replace-cross
Abstract
Adverse weather conditions, low-light environments, and bumpy road surfaces pose significant challenges to SLAM in robotic navigation and autonomous driving. Existing datasets in this field predominantly rely on single sensors or combinations of LiDAR, cameras, and IMUs. However, 4D millimeter-wave radar demonstrates robustness in adverse weather, infrared cameras excel in capturing details under low-light conditions, and depth images provide richer spatial information. Multi-sensor fusion methods also show potential for better adaptation to bumpy roads.
Despite some SLAM studies incorporating these sensors and conditions, there remains a lack of comprehensive datasets addressing low-light environments and bumpy road conditions, or featuring a sufficiently diverse range of sensor data. In this study, we introduce a multi-sensor dataset covering challenging scenarios such as snowy weather, rainy weather, nighttime conditions, speed bumps, and rough terrains.
Dataset Overview
The DIDLM dataset includes rarely utilized sensors for extreme conditions, such as:
- 4D millimeter-wave radar
- Infrared cameras
- Depth cameras
- 3D LiDAR
- RGB cameras
- GPS
- IMU
This diverse array of sensors supports both autonomous driving and ground robot applications, providing reliable GPS/INS ground truth data and covering structured and semi-structured terrains.
Evaluation of SLAM Algorithms
We evaluated various SLAM algorithms using this dataset, incorporating:
- RGB images
- Infrared images
- Depth images
- LiDAR
- 4D millimeter-wave radar
The dataset spans a total of 18.5 km, 69 minutes, and approximately 660 GB, offering a valuable resource for advancing SLAM research under complex and extreme conditions.
Availability
Researchers and practitioners interested in exploring this dataset can access it at the following link: DIDLM Dataset.
In summary, the DIDLM dataset addresses the critical need for a multi-sensor dataset that accommodates the challenges posed by adverse weather, low-light conditions, and rough terrains. This advancement holds promise for improving SLAM research and applications in navigating real-world scenarios.