corridor-alignment-pipeline

Long corridor alignment requires incremental batch processing with automatic drift detection and QA gates to prevent divergence. Processing cameras in batches of 200 with GPS reference data allows drift-aware checks after each batch to stop and notify users when continuity issues arise.

Incremental batch alignment processes long linear road corridor captures in sequential batches with drift checks after every batch. GPS reference data drives automatic drift assessment and continuity evaluation between batches, with QA gates halting progression when divergence is detected to prevent wasted processing time.

GPS reference data enables automatic drift detection and continuity assessment during corridor alignment via the Metashape MCP server. The pipeline applies GPS and sensor configuration to evaluate drift between incremental batches, triggering QA gates that stop progression when divergence or discontinuity is detected.

Processing 100 or more cameras for corridor alignment uses incremental batches, such as 200 cameras per batch, with automatic drift checks after each batch. GPS reference data drives continuity assessment between batches, and QA gates stop progression when drift is detected to prevent wasted processing time.

Photogrammetry alignment drift in long linear corridors occurs when incremental processing accumulates positional errors across sequential batches. Automatic drift detection using GPS reference data identifies divergence between batches, and QA gates halt progression to prevent continuity loss and wasted processing time on misaligned captures.

Batch-based corridor alignment with drift guards requires GPS reference data and the Metashape MCP server to function. QA gates will halt progression when drift or discontinuity is detected between batches, requiring user intervention and mitigation before alignment can resume on long corridor captures.