> ## Documentation Index > Fetch the complete documentation index at: https://avala.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # Core Concepts > Understand the fundamental concepts of the Avala platform This page covers the building blocks of the Avala platform: visualization, annotation, datasets, projects, tasks, organizations, labels, quality control, and sequences. Understanding these concepts will help you design effective data workflows for physical AI. ## Visualization Avala provides GPU-accelerated visualization for sensor data directly in the browser. These concepts apply across all visualization features. ### Viewers Avala includes specialized viewers for different data types. The **multi-sensor viewer** handles MCAP and ROS recordings with synchronized playback across all sensor streams. The **3D point cloud viewer** renders LiDAR data with six visualization modes. The **Gaussian Splat viewer** renders photorealistic 3D scene reconstructions using WebGPU. ### Panels The multi-sensor viewer organizes data into **panels** — independent visualization windows for different data streams. Avala supports eight panel types: | Panel Type | Description | | ----------------- | ------------------------------------ | | Image | Camera frames and image streams | | 3D / Point Cloud | LiDAR scans and 3D geometry | | Plot | Time-series data and numeric signals | | Raw Messages | Decoded message payloads | | Log | Textual log streams | | Map | Geographic position and trajectories | | Gauge | Real-time numeric readouts | | State Transitions | Discrete state changes over time | Topics are automatically assigned to panels based on their schema. ### Layouts Multi-window layouts arrange panels in a configurable grid. The **layout composer** automatically builds optimized arrangements based on the topics in your data, or you can customize the layout manually by dragging, resizing, and rearranging panels. ### Timelines All panels in a viewer share a **synchronized timeline**. Navigate frame-by-frame, scrub to specific timestamps, or play back recordings at configurable speeds. The timeline keeps all sensor streams aligned regardless of their individual capture frequencies. ### Sensor Streams MCAP recordings contain multiple **sensor streams** (topics). Each topic carries a specific data type — images, point clouds, IMU readings, GPS coordinates — at its own frequency. Avala synchronizes all streams by timestamp so you can see the full sensor picture at any moment in time. ### Visualization Modes Point cloud data can be colored using six modes: | Mode | Description | | ---------------- | -------------------------------------- | | Neutral | Single uniform color | | Intensity | Colored by return strength | | Rainbow | Temporal or sequential coloring | | Label | Colored by semantic class | | Panoptic | Colored by instance identity | | Image Projection | Textured with projected camera imagery | Visualization modes apply to the 3D point cloud viewer and work with both standalone LiDAR datasets and point cloud streams within MCAP recordings. ## Datasets A **dataset** is a collection of data items (images, video frames, point clouds, or multi-sensor recordings) that serve as the raw material for visualization and annotation. ### Dataset Properties | Property | Description | | ------------ | -------------------------------------------------------------------- | | `name` | Human-readable name | | `slug` | URL-friendly identifier (unique within the owner's namespace) | | `data_type` | Type of data: `image`, `video`, `lidar`, `mcap`, `image_3d`, `splat` | | `visibility` | `public` or `private` | | `owner` | User or organization that owns the dataset | | `item_count` | Total number of data items in the dataset | ### Data Items Each dataset contains **items** — individual data samples: * **Image datasets** — Each item is a single image file. * **Video datasets** — Items are video frames, grouped into sequences. * **LiDAR datasets** — Items are individual point cloud scans. * **MCAP datasets** — Items contain synchronized multi-sensor frames (camera + LiDAR + IMU). ### Sequences **Sequences** group related items for temporal or multi-frame data: * Video frames from the same recording * LiDAR scans from a continuous driving session * Synchronized multi-camera captures at consecutive timestamps Sequences enable frame-by-frame navigation, object tracking across frames, and temporal consistency in annotations. Sequence status workflow: ``` uploading → processing → ready → failed ``` ## Projects A **project** defines an annotation workflow by connecting one or more datasets to a specific task type, label taxonomy, and quality control configuration. ### Project Components ``` Project ├── Datasets (data sources) ├── Task Type (annotation method) ├── Label Config (object classes, attributes) ├── Quality Control (review stages, consensus) └── Tasks (individual work units) ``` ### Task Types Projects are configured with one of the following task types: | Task Type | API Value | Description | | ---------------------- | ------------------------ | ------------------------------------------------------------------------- | | Image Annotation | `image-annotation` | 2D annotation on single images (boxes, polygons, segmentation, keypoints) | | Video Annotation | `video-annotation` | Frame-by-frame annotation with object tracking across frames | | Point Cloud Annotation | `point-cloud-annotation` | 3D annotation on LiDAR scans (cuboids, segmentation) | | Point Cloud Objects | `point-cloud-objects` | Object-level annotation in 3D point cloud sequences | ### Project Status | Status | Description | | ------------------ | ---------------------------------------- | | `pending-approval` | Awaiting approval to start | | `active` | Accepting annotation work | | `paused` | Temporarily halted | | `canceled` | Permanently stopped | | `archived` | Completed and archived | | `completed` | All annotation tasks have been completed | ## Tasks A **task** is an individual work unit within a project. Each task represents annotation work to be done on one or more data items by a single annotator. ### Task Lifecycle Tasks progress through the following states: ``` pending → assigned → in_progress → submitted → under_review → approved → rejected → rework ``` | Status | Description | | -------------- | --------------------------------------------------- | | `pending` | Created but not yet assigned to an annotator | | `assigned` | Assigned to an annotator, waiting for them to start | | `in_progress` | Annotator is actively working on the task | | `submitted` | Annotator has submitted their work for review | | `under_review` | A reviewer is examining the submitted annotations | | `approved` | Annotations accepted — task is complete | | `rejected` | Annotations did not pass review | | `rework` | Returned to the annotator for corrections | ### Results When an annotator completes a task, they submit a **result** containing: * The annotation data (bounding boxes, polygons, cuboids, segmentation masks, etc.) * Metadata (time spent, tool versions) Results go through quality control review before final acceptance. ## Organizations An **organization** groups users and resources for team-based collaboration. ### Organization Structure ``` Organization ├── Members (users with roles) ├── Datasets (shared data) ├── Projects (shared workflows) └── Settings (billing, API keys, permissions) ``` ### Member Roles | Role | Capabilities | | -------- | ------------------------------------------------------------- | | `owner` | Full control — billing, settings, can delete the organization | | `admin` | Manage members, create and configure resources | | `member` | Access shared resources, perform annotation work | ## Labels and Taxonomy ### Label Config Projects define a **label config** — a set of predefined object classes that annotators assign to annotations: ```json theme={null} { "labels": [ { "name": "car", "color": "#FF0000" }, { "name": "pedestrian", "color": "#00FF00" }, { "name": "cyclist", "color": "#0000FF" } ] } ``` ### Classification For more complex taxonomies, projects can include **classification configs** that define: * **Attributes** — Properties like color, occlusion level, or truncation that annotators assign to each object. * **Hierarchical categories** — Nested class structures (e.g., Vehicle > Car > Sedan). * **Conditional attributes** — Attributes that only appear for specific object classes. ## Annotation Types Avala supports the following annotation types, each designed for specific labeling tasks: | Type | Description | Data Types | | ------------------ | ------------------------------------------------------- | ------------------ | | **Bounding Box** | 2D rectangular region around an object | Images, Video | | **Polygon** | Arbitrary closed shape tracing object boundaries | Images, Video | | **3D Cuboid** | 3D bounding box with position, dimensions, and rotation | Point Clouds, MCAP | | **Segmentation** | Pixel-level classification mask | Images, Video | | **Polyline** | Open path for lanes, edges, and boundaries | Images, Video | | **Keypoints** | Landmark points for pose estimation and structure | Images, Video | | **Classification** | Scene-level or object-level categorical labels | All data types | ## Quality Control Avala provides built-in quality assurance tools to ensure annotation accuracy and consistency. ### Reviews Annotations go through a **review stage** before acceptance: 1. Annotator submits their result. 2. A reviewer examines the annotations. 3. The reviewer **approves** correct work or **rejects** work that needs correction. 4. Rejected tasks return to the annotator for rework. ### Issues **Annotation issues** let reviewers flag specific problems on individual annotations: * Pin an issue to a specific object or region in the scene. * Assign issues to team members for resolution. * Track issue status (open, resolved). ### Metrics Monitor annotation quality with built-in metrics: * **Acceptance rate** — Percentage of tasks approved on first submission. * **Annotation time** — Average time spent per task. * **Inter-annotator agreement** — Consistency across annotators on the same data. * **Issue frequency** — Rate of flagged problems per task. ### Consensus **Consensus workflows** assign the same data to multiple annotators independently, then compare results to measure agreement and identify ambiguous cases. Quality metrics help identify training needs and maintain consistent annotation standards across your team. ## Sequences **Sequences** are ordered collections of data items that represent temporal or spatial progressions — video frames, LiDAR sweeps, or multi-sensor recordings. ### Properties | Property | Description | | ------------- | --------------------------------- | | `name` | Sequence identifier | | `frame_count` | Number of frames in the sequence | | `status` | Processing status of the sequence | | `data_type` | Inherited from the parent dataset | ### Status Workflow Sequences follow this status progression as data is uploaded and processed: ``` uploading → processing → ready → failed ``` * **uploading** — Frames are being uploaded to the platform. * **processing** — Frames are being validated and prepared for annotation. * **ready** — All frames are processed and available for annotation. * **failed** — Processing encountered an error (check individual frame statuses). ## Fleet Management Fleet Management is in preview. Features described here may change. Avala's fleet management capabilities let you manage devices, recordings, and telemetry across robot fleets at scale. ### Devices A **device** represents a physical robot, sensor rig, or compute unit in your fleet. Each device has a unique `dev_` prefixed identifier and tracks metadata like type, firmware version, and status (`online`, `offline`, `maintenance`). ### Recordings Devices produce **recordings** — MCAP files captured during operation. Recordings are automatically associated with their source device and can be filtered by device, date, status, and tags. ### Events **Events** are timestamped markers on recordings: errors, state changes, anomalies, and custom annotations. Events appear on the MCAP viewer timeline and can be queried across the fleet. ### Recording Rules **Recording rules** automatically evaluate recordings against conditions and take actions (tag, flag for review, notify) when matches occur. Rules can trigger on thresholds, patterns, frequencies, or data absence. ### Alerts **Alerts** notify your team when fleet conditions change. Route alerts to Slack, email, webhooks, or in-app notifications. Alerts follow a lifecycle: `open` → `acknowledged` → `resolved`. ## Next Steps

Data Types

Supported formats, visualization capabilities, and annotation tools for each data type.

Annotation

Learn the web interface for visualization, annotation, and project management.

Architecture

How the Avala platform components fit together, including the visualization engine.

API Authentication

Set up API keys and start making authenticated requests.

Fleet Dashboard

Manage devices, recordings, and telemetry across your robot fleet.