Multimodal data visualization refers to the unified display and analysis of diverse robotics data types—sensor streams, logs, camera feeds, 3D models, and telemetry—across formats like URDF, ROS Bags, Protobuf, and MCAP. Foxglove enables developers to centralize and interpret this data in real-time, improving decision-making, debugging speed, and development velocity.
Challenges with traditional visualization tools.
Legacy tools fall short in robotics development:
- Fragmented ecosystems across different data formats
- Lack of real-time indexing and visualization
- Poor support for robotics-specific workflows (e.g., sensor fusion, time-synced playback)
- Manual switching between tools delays debugging and insight gathering
Foxglove solves this by consolidating multimodal data into a single, purpose-built platform.
Visual Insight: See what your robots see.
Foxglove redefines observability by offering:
- A unified workspace for viewing telemetry, logs, video, and 3D environments
- Collaborative layouts that simplify team-based debugging
- Support for custom panels tailored to your robot’s unique needs
Developers can trace how robots sense, think, and act through synchronized timelines and configurable views.
How Foxglove’s multimodal visualization works.
Foxglove’s system operates in 3 core phases:
- Data ingestion: Ingest MCAP, ROS Bags, and other formats via streaming or file import from edge devices and local environments.
- Real-Time Indexing: As data is streamed or uploaded, Foxglove indexes it by time, topic, and device—enabling granular queries and synced playback.
- Panel-Based Visualization: Use shared layouts and specialized panels (e.g., 3D, plots, camera) to explore your robot’s behavior across time and space.
🔗 Supported Formats
🔗 How to Import Data
Key benefits for robotic and physical AI development.
- Accelerated Debugging: Reduce time-to-insight with real-time data introspection
- Custom Extensibility: Build domain-specific tools via custom React panels
- Improved Collaboration: Share layouts and insights across teams instantly
- Scalable Observability: Handle increasing data complexity and volume with a cloud-native backend
Who should use multimodal visualization?
Foxglove benefits a wide range of roles:
- Roboticists managing sensor-rich, multi-system robots
- Data Scientists optimizing perception and decision algorithms
- Product Teams needing visibility into autonomous behavior
- Organizations scaling from prototypes to fleet-wide deployments
In high-stakes robotics environments, faster debugging and deeper insights drive real-world performance improvements.
