Examples

Visualize real-world robotic data using Foxglove.

Click on an example multimodal dataset below to visualize it directly in Foxglove.

Waymo

Autonomous Vehicle

The field of machine learning is changing rapidly. Waymo is in a unique position to contribute to the research community, by creating and sharing some of the largest and most diverse autonomous driving datasets.

DROID

Autonomous Robotic Manipulation

The DROID dataset trained using DepthAnythingV2 models plus the integration of time-series data from the Franka Robotics Panda Arm visualized using Foxglove. With parameter scales from 25M to 1.3B DepthAnythingV2 demonstrates strong generalization and broad applicability, achieving fine-grained details and enhanced depth accuracy.

More examples

Quadruped

For those who haven’t met us live at a conference, we’d like to introduce Bodi, Foxglove’s unofficial “mascot” – a Unitree Go1 visualized using Foxglove. 🐾

Autonomous Vehicle

The nuScenes dataset (pronounced /nu:si:nz/) is a public large-scale dataset for autonomous driving developed by the team at Motional (formerly nuTonomy).

Drone

The UZH-FPV drone racing dataset provides the most aggressive visual-inertial odometry, high-speed 6DoF trajectories for state estimation, large accelerations, and rotations to date.

Quadruped

This dataset is a short validation of the Boston Dynamics Spot® robot, combining the URDF with onboard sensor data, including recorded velocities from onboard odometry and contact state transitions for each foot… or paw in this case perhaps?!

Mobile Robot

SLAM (Simultaneous Localization and Mapping) is crucial for autonomous robotic systems, enabling efficient navigation and mapping in unknown environments.

Humanoid

NASA’s Valkyrie humanoid was designed and built to be a robust, rugged, entirely electric humanoid robot with 44 DoF, capable of operating in degraded or damaged human-engineered environments. This is a simulation of it lifting an item… perhaps in space?

Autonomous Robotic Manipulation

Simulation plays a crucial role in robotics development by allowing developers to assess their algorithms and designs without physical prototypes. This dataset simulates a Universal Robots UR5e robotic arm with physics-based simulator Gazebo.

Unmanned Ground Vehicle

Using a Clearpath A200 Husky mobile robot equipped with a Velodyne HDL32 lidar sensor, an Xsens MTI-30 IMU, and wheel encoders, the research team overcomes the limitations of manual agriculture, allowing them to cover far more ground.

Autonomous Underwater Vehicle

SubPipe is a public dataset of a submarine outfall pipeline, property of Oceanscan-MST. This dataset was acquired with a Light Autonomous Underwater Vehicle by Oceanscan-MST, within the scope of Challenge Camp 1 of the H2020 REMARO project.

Mobile Robot

Hilti Robot SLAM challenge with robot-mounted sensor suite consisting of a Robosense BPearl hemisphere lidar, an Xsens MTi-670 IMU and 4x OAK-D cameras.

Autonomous Mobile Robot

Treescope provides ground-truth data (including sensor data from lidar, IMU, GPS, RGBD and thermal cameras) for semantic segmentation and diameter estimation from agricultural environments to address the counting and mapping of trees in forestry and orchards.

Unmanned Ground Vehicle

This unmanned ground vehicle (UGV) is an effective multi-cue positioning system used for self-localization, pose estimation, and mapping capabilities in farming and agricultural scenarios; providing lidar, IMU, GPS, RGBD, thermal cameras, time-series data, and more.

Autonomous Vehicle

The renowned KITTI-360 dataset is instrumental in different applications, including 3D scene reconstruction, object detection, and semantic segmentation.

Handheld

The integration of RGB-D cameras in robotics for generating dense 3D maps of indoor environments is crucial for robot navigation, manipulation, semantic mapping, and telepresence.

Unmanned Ground Vehicle

The Rellis-3D dataset features multiple sensors, including a 64-beam Ouster lidar, a 32-beam Velodyne lidar, a Nerian Karmin2 Stereo Depth Camera (without RGB), a Nerian Stereo Depth Camera (without RGB), and a Vectornav VN-300 Dual Antenna GNSS/INS (Inertial Navigation System with GPS/IMU).

Handheld

The Hilti Handheld SLAM challenge dataset features a sensor suite equipped with a Sevensense Alphasense Core camera head with five 0.4MP global shutter cameras and a Hesai PandarXT-32.