Visualization for Robotics Data
Robotics sensor data often arrives as recorded bags or MCAP files from test runs, field deployments, or simulation. Avala’s multi-sensor viewer lets you play back these recordings and inspect them before committing to annotation.MCAP Playback
Upload MCAP recordings from your robot and play back camera, depth, LiDAR, and IMU streams in a synchronized viewer.
Point Cloud Visualization
Render point clouds from depth cameras and LiDAR with GPU acceleration. Switch between 6 visualization modes to inspect density, intensity, and spatial structure.
Multi-Camera Views
View multiple camera streams (RGB, depth, stereo) side by side, synchronized to the same timestamps in the recording.
Timeline Navigation
Step frame-by-frame through robot operations to find key moments — grasp attempts, navigation decisions, collision events.
Data Types
Common Tasks
Object Detection and Grasping
Label objects on shelves, tables, and conveyor belts with bounding boxes and instance segmentation masks for grasp planning models. For bin-picking tasks, combine bounding boxes with keypoint annotations to mark grasp points on each object.Scene Segmentation
Create pixel-level segmentation masks for floors, walls, obstacles, free space, and other surface types. Segmentation data trains navigation models to understand which areas the robot can traverse and which are blocked.Keypoint Annotation
Mark joint positions, tool tips, grasping points, and pose landmarks. Keypoint skeletons are configurable — define the number of points and their connections to match your model’s expected input.Terrain Classification
For outdoor mobile robots, classify traversable vs. non-traversable surfaces. Combine image-level classification (terrain type, slope) with segmentation masks that delineate safe zones from obstacles.Activity and Event Detection
Annotate video recordings of robot operations to label specific events: successful grasp, failed grasp, collision, recovery. Use classification labels on sequences or frame ranges for temporal event annotation.Avala Features Used
Example Pipeline
Getting Started
1
Upload robot recordings
Create a dataset and upload your MCAP files. The viewer automatically detects camera, depth, LiDAR, and IMU topics.
2
Explore the data
Play back recordings to understand sensor coverage and data quality. Use frame-by-frame navigation to find key moments.
3
Define your annotation task
Choose the annotation type that matches your model’s input: bounding boxes for detection, keypoints for pose estimation, segmentation for scene understanding.
4
Set up label taxonomy
Define object classes and attributes relevant to your robot’s task environment (e.g.,
cup, plate, obstacle, free_space).5
Annotate, review, and export
Your team labels the data, reviewers check quality, and you export in the format your training pipeline expects.
Fleet Management
For teams operating robot fleets, Avala provides fleet-scale recording management and observability:- Device registry — Track all robots in your fleet with metadata, firmware versions, and health status.
- Recording browser — Filter and sort recordings across devices by date, status, and tags.
- Timeline events — Mark errors, anomalies, and state changes on recordings for fleet-wide analysis.
- Recording rules — Auto-flag recordings matching conditions (e.g., high latency, error frequency).
- Alerts — Route notifications to Slack, email, or webhooks when fleet conditions change.
Next Steps
Fleet Dashboard
Manage devices, recordings, and telemetry across your robot fleet.
MCAP & ROS
Supported formats and how to prepare robot recordings for upload.
Annotation Tools
Overview of all annotation tools available for robotics data.
Recording Best Practices
Tips for recording robot data that works well in Avala.