For more information: Please refer to the Super Odometry Website

SubT-MRS Dataset

The SubT-MRS Dataset is an exceptional real-world collection of challenging datasets obtained from Subterranean Environments, encompassing caves, urban areas, and tunnels. Its primary focus lies in testing robust SLAM capabilities and is designed as Multi-Robot Datasets, featuring UGV, UAV, and Spot robots, each demonstrating various motions. The datasets are distinguished as Multi-Spectral, integrating Visual, Lidar, Thermal, and inertial measurements, effectively enabling exploration under demanding conditions such as darkness, smoke, dust, and geometrically degraded environments.Key features of our dataset:

  • Multiple Modalities: Our dataset includes hardware time-synchronized data from 4 RGB cameras, 1 LiDAR, 1 IMU, and 1 thermal camera, providing diverse and precise sensor inputs.

  • Diverse Scenarios Collected from multiple locations, the dataset exhibits varying environmental setups, encompassing indoors, outdoors, mixed indoor-outdoor, underground, off-road, and buildings, among others.

  • Multi-Degraded By incorporating multiple sensor modalities and challenging conditions like fog, snow, smoke, and illumination changes, the dataset introduces various levels of sensor degradation.

  • Heterogeneous Kinematic Profiles: The SubT-MRS Dataset uniquely features time-synchronized sensor data from diverse vehicles, including RC cars, legged robots, drones, and handheld devices, each operating within distinct speed ranges.

TartanAir Dataset

This benchmark is based on the TartanAir dataset, which is collected in photo-realistic simulation environments based on the AirSim project. A special goal of this dataset is to focus on the challenging environments with changing light conditions, adverse weather, and dynamic objects. The four most important features of our dataset are:

  • Large size diverse realistic data. We collect the data in diverse environments with different styles, covering indoor/outdoor, different weather, different seasons, urban/rural.
  • Multimodal ground truth labels. We provide RGB stereo, depth, optical flow, and semantic segmentation images, which facilitates the training and evaluation of various visual SLAM methods.
  • Diversity of motion patterns. The existing popular datasets such as KITTI and Cityscapes only cover very limited motion patterns, which are mostly moving straight forward plus small left or right turns. This regular motion is too simple to sufficiently test a visual SLAM algorithm. Our dataset covers much more diverse motion combinations in 3D space, which is significantly more difficult than existing datasets.
  • Challenging Scenes. We include challenging scenes with difficult lighting conditions, day-night alternating, low illumination, weather effects (rain, snow, wind and fog) and seasonal changes.

For more information: Please refer to the Super Odometry Website

Robustness and real-time perforamnce on robots are what we cared about.