RELLIS-3D is a multi-modal dataset for off-road robotics. It was collected in an off-road environment containing annotations for 13,556 LiDAR scans and 6,235 images. The data was collected on the Rellis Campus of Texas A&M University and presents challenges to existing algorithms related to class imbalance and environmental topography. The dataset also provides full-stack sensor data in ROS bag format, including RGB camera images, LiDAR point clouds, a pair of stereo images, high-precision GPS measurement, and IMU data. Source: https://github.com/unmannedlab/RELLIS-3D Image Source: https://github.com/unmannedlab/RELLIS-3D
A novel egocentric dataset collected from social mobile manipulator JackRabbot. The dataset includes 64 minutes of annotated multimodal sensor data including stereo cylindrical 360 degrees RGB video at 15 fps, 3D point clouds from two Velodyne 16 Lidars, line 3D point clouds from two Sick Lidars, audio signal, RGB-D video at 30 fps, 360 degrees spherical image from a fisheye camera and encoder values from the robot's wheels. Source: JRDB: A Dataset and Benchmark of Egocentric Visual Perception for Navigation in Human Environments
Many existing datasets for lidar place recognition are solely representative of structured urban environments, and have recently been saturated in performance by deep learning based approaches. Natural and unstructured environments present many additional challenges for the tasks of long-term localisation but these environments are not represented in currently available datasets. To address this we introduce Wild-Places, a challenging large-scale dataset for lidar place recognition in unstructured, natural environments. Wild-Places contains eight lidar sequences collected with a handheld sensor payload over the course of fourteen months, containing a total of 63K undistorted lidar submaps along with accurate 6DoF ground truth. This dataset contains multiple revisits both within and between sequences, allowing for both intra-sequence (i.e., loop closure detection) and inter-sequence (i.e., re-localisation) tasks. We also benchmark several state-of-the-art approaches to demonstrate the challenges that this dataset introduces, particularly the case of long-term place recognition due to natural environments changing over time. Our dataset and code is available at https://csiro-robotics.github.io/Wild-Places
MIDGARD is an open-source simulator for autonomous robot navigation in outdoor unstructured environments. It is designed to enable the training of autonomous agents (e.g., unmanned ground vehicles) in photorealistic 3D environments, and support the generalization skills of learning-based agents thanks to the variability in training scenarios.
IN2LAAMA is a set of lidar-inertial datasets collected with a Velodyne VLP-16 lidar and a Xsens MTi-3 IMU. Source: IN2LAAMA: INertial Lidar Localisation Autocalibration And MApping
Towards automated analysis of large environments, hyperspectral sensors must be adapted into a format where they can be operated from mobile robots. In this dataset, we highlight hyperspectral datacubes collected from the Hyper-Drive imaging system. Our system collects and registers datacubes spanning the visible to shortwave infrared (660-1700 nm) in 33 wavelength channels. The system also simultaneously captures the ambient solar spectrum reflected off a white reference tile. The dataset consists of 500 labeled datacubes from on-road and off-road terrain compliant with the ATLAS. This work first appeared at WHISPERS 2023 In Athens, Greece and is a product of the Robotics and Intelligent Vehicles Research Laboratory (RIVeR) at Northeastern University. In addition to the 500 labeled hyperspectral datacubes, raw ROS bagfiles generated of each of the sensor feeds at a higher frame rate are available here. These files are provided as an additional resource and do not contain semantic labels, but contain ~10,000 additional hyperspectral datacubes of in-between frames from the labeled dataset. It also contains additional datatypes for terrain analysis such as inertial measurement unit (IMU) data. To the best of the authors knowledge, it is the largest vehicle-centric hyperspectral dataset currently available! This research was sponsored by the United States Army Core of Engineers (USACE) Engineer Research and Development Center (ERDC) Geospatial Research Laboratory (GRL) and was accomplished under Cooperative Agreement Federal Award Identification Number (FAIN) W9132V-22-2-0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of USACE EDRC GRL or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.