Table of Contents

Multi-time slot fusion solution

Multi-time slot fusion positioning is an advanced feature designed to ensure persistent Mega experience in complex lighting environments. By constructing map collections covering different time periods, the system overcomes visual feature interference caused by day-night alternation and seasonal changes, ensuring centimeter-level precision positioning at any time of day.

Core challenge

Mega primarily relies on environmental visual features for positioning. Although algorithms have been optimized for lighting and seasonal variations, drastic illumination differences from day-night transitions may fundamentally alter visual features at the same location. Therefore, map data collected at a single time point (e.g., daytime only) often fails to match during another period (e.g., nighttime) due to significant feature differences, resulting in positioning failure.

Solution

To enable all-weather positioning, the Mega platform provides multi-time slot data fusion capability. By processing data from different periods in the cloud, the system further enhances adaptability to illumination changes.

How it works

  1. Multi-time slot collection: Collect data for the same physical scene under representative lighting conditions (e.g., daytime, nighttime).
  2. Cloud data fusion: Upload all collected data to the Mega cloud. Cloud services automatically process multi-period data to build a map database covering different time slots through feature fusion optimization.
  3. Automatic matching & positioning: During application runtime, the system automatically retrieves and matches the map closest to current lighting conditions from the fused database based on real-time camera images, then returns the image's pose in the map.

Best practices

For optimal fusion results, follow these collection specifications:

  • Cover key periods: Include at least one "daytime" and one "nighttime" dataset. For scenes with extreme lighting changes (e.g., squares with scheduled landscape lighting), add "pre-lighting" and "post-lighting" collections.
  • Path consistency: Maintain consistent walking paths and shooting angles across different collection times to facilitate efficient feature alignment and fusion in the cloud.

Implementation workflow

Enable multi-time slot fusion positioning by following this specific collection and configuration workflow.

  1. Collection planning
    Evaluate scene lighting variations to determine required time slot combinations:

    • Basic combination: One daytime dataset + one nighttime dataset (recommended after full streetlight activation)
    • Enhanced combination: Add dusk data if special lighting exists during high-traffic twilight hours

  2. Data collection
    Ensure identical walking paths and shooting angles for each time slot. Example: If collecting along a street centerline south-to-north during daytime, replicate this path at night. This significantly improves map alignment accuracy by helping the cloud compute geometric relationships between period-specific maps.
    Before multi-period collection:

  3. Map construction

  4. Review mapping results
    After mapping completion, inspect results including collection routes and spatial models:

    Tip

  5. Test positioning performance

Important

Reminder: Maintain identical walking paths and shooting angles during each collection period. This helps the cloud efficiently compute spatial relationships between sub-regions and improves map alignment accuracy.

Tip

Multi-period maps are strictly aligned through optimized fusion. Annotation content only needs placement once.