Markercollection

Markercollection refers to a data structure or module designed to hold a set of markers, which are distinct items used to tag locations, objects, or events in digital spaces. Markers are typically characterized by a unique identifier and attributes such as pose (position and orientation), type (for example, fiducial marker, QR code, or feature marker), size, and optional metadata. A markercollection provides organization and efficient access for downstream tasks such as tracking, mapping, or recognition.

Data model: Each marker includes id, type, pose, size, timestamp, and confidence score. The collection may index markers by id for quick retrieval and optionally build spatial indices to support proximity searches or range queries. Implementations may store markers in arrays, hash maps, or spatially aware structures like k-d trees or R-trees.

Operations and behavior: Typical operations include addMarker, removeMarker, updateMarker, getMarker, and query methods such as findById, findNearby, or filterByType. Serialization to interchange formats such as JSON or binary forms is common for persistence or communication between components. Thread-safety and mutation semantics vary by platform.

Applications: Markercollections are used in robotics for landmark tracking, augmented reality to stabilize overlays, computer vision datasets to organize labeled points, geospatial applications managing map markers, and educational tools that annotate locations of interest.