What sensor data can Avala label for robotics?
Avala publishes workflows for cameras, video, LiDAR, point clouds, depth maps, and sequences, and it imports MCAP, ROS 1, and ROS 2 recordings with synchronized camera, LiDAR, and telemetry streams.
Sensor data labeling
Robotics Sensor Data Labeling Software
Avala labels camera, LiDAR, point-cloud, and sequence data for robotics, then keeps that work inside the fleet learning loop: ingest, review, a versioned dataset, and the training handoff.
Labeling is one stage of the loop Avala runs, not the category.
Published robotics labeling capabilities
| Capability | Status | What Avala publishes |
|---|---|---|
| Cameras and video | Supported | Images and video are labeled with 2D boxes and masks, alongside the sequence they belong to. |
| LiDAR and point clouds | Supported | Point-cloud scenes take 3D cuboids and point-cloud labels, with camera and sequence context kept where the workflow supports them. |
| Depth maps | Supported | Depth maps are a published annotation type on the same workflow as video, point clouds, text, and audio. |
| MCAP, ROS 1, and ROS 2 | Supported | Upload MCAP, ROS 1 bag, or ROS 2 bag recordings and inspect synchronized camera, LiDAR, and telemetry streams. |
| Timestamp and calibration context | Preserved | Timestamp and calibration context stay attached while teams inspect the same event across sensor streams. |
| 2D boxes, masks, polylines, and classifications | Supported | These are published ground-truth outputs for camera and sequence data. |
| 3D cuboids | Supported | Cuboids capture position, dimensions, and heading in point-cloud scenes. |
| Tracking across a sequence | Supported | Tracked objects are a published output, so an object keeps its identity across the sequence. |
| Trajectory context | Supported | Camera, point-cloud, and trajectory data stay aligned as work moves through labeling and review. |
| LeRobot datasets | Supported | Import and export LeRobot datasets through the Python and TypeScript SDKs. |
Read the supporting pages
The pages below are the detailed sources for the capabilities in the table.
Questions this page answers
Is labeling the whole product?
Labeling is one stage of the loop Avala runs, not the category. Avala is fleet learning infrastructure for Physical AI: ingest, visualization, human and AI labeling, quality control, dataset versions, and the training handoff.
How is labeling reviewed?
Labeling runs with human review and traceable quality-control states. The annotation pages describe the review path for camera, LiDAR, and telemetry.
How do I start a robotics labeling workflow?
Book a demo, or read the ground-truth outputs and the MCAP annotation page. LeRobot import and export are documented with the SDKs.