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Best Workflow for Registering Multiple TVGO Cycles

Darrick Wagg
  • Updated
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When you have one or more RTK or PPK processed Cycle from a handheld scanner, such the TrueView GO, with GCPs (outdoor + indoor) and additional indoor cycles without control points, the recommended approach is:

  1. Start with the georeferenced Cycle
    • Import the RTK or PPK Cycle into LP360 as your base project. This Cycle provides the absolute reference frame because it includes surveyed GCPs and GNSS corrections.
    • Perform QA/QC checks (trajectory fixed solution, strip alignment, noise filtering) before moving forward.
  2. Add additional cycles into the same project
    • Use Cycle Import TrueViewIcon_Import Wizard.pngin LP360 to bring in the indoor cycles. These will initially be in their local SLAM coordinate system.
  3. Registration Options

❓ Do You Need Common Control Points in Every Cycle?

  • Not necessarily. If at least one Cycle is georeferenced (with GCPs or RTK), others can be registered relatively using overlapping geometry.
  • Common control points improve accuracy but are optional if overlap is sufficient and SLAM performed well.

🛠️ Using TVGO for Common Points

  • Yes, you can capture common features with TrueView GO and use them as tie points for manual registration.
  • How many? At least 3 well-distributed points per cycle for rigid registration (translation + rotation). More points (5–6) improve accuracy and allow scale adjustment if needed.

🔍 Registering Indoor Cycles Without Control Points

  • If cycles share significant overlap (walls, door frames, structural features), cloud-to-cloud registration using an Iterative Closest Point (ICP) approach with the LAS-to-LAS Manual Registration TrueViewIcon_LAS2LAS_ManualRegistration.png followed by LAS-to-LAS Auto Registration TrueViewIcon_LAS2LAS_AutoRegistration.png workflows works well.
  • Accuracy depends on overlap quality and SLAM trajectory stability. Expect some drift correction during alignment.

🧠 What is ICP

ICP stands for Iterative Closest Point. It’s a widely used algorithm for aligning two point clouds by minimizing the distance between corresponding points.

How ICP Works:

  1. Initial Alignment
    • You start with two point clouds: a reference (base cycle) and a target (additional cycle).
    • An initial guess of their relative position is required (often based on GNSS or SLAM trajectory).
  2. Iterative Process
    • For each iteration:
      • Find the closest points between the two clouds.
      • Compute the transformation (translation + rotation) that minimizes the distance between these pairs.
      • Apply the transformation to the target cloud.
    • Repeat until convergence (error change is below a threshold).
  3. Output
    • A rigid transformation that best aligns the two point clouds.
    • Works well when there is significant overlap and the geometry is distinctive.

Why ICP is Important Here:

  • It allows you to register indoor cycles without control points by leveraging overlapping geometry.
  • Common in SLAM workflows and tools like LP360 for LAS-to-LAS registration.

📎Practical Tips

  • Distribute tie points across the entire area (not clustered).
  • Avoid repetitive patterns (e.g., identical doors) as they can confuse algorithms.
  • Validate alignment visually and check RMSE in LP360 after registration.
  • For large projects, consider global + local registration (FGR + ICP) for better convergence.

Here’s a comparison of workflows with GCPs vs. without GCPs for registering multiple LiDAR Cycles into one georeferenced point cloud:

✅ Workflow with GCPs

Best for: High-accuracy deliverables (survey-grade).

Steps:

  1. Base Cycle Setup
    • Use the RTK or PPK Cycle with surveyed GCPs as the reference.
    • Apply GNSS corrections and verify fixed solution.
  2. Control Point Registration
    • Import GCPs into LP360 and run LAS-to-Control adjustment.
    • Check RMSE and residuals for each control point.
  3. Additional Cycles
    • Import indoor Cycles.
    • Register them using common control points or overlapping geometry.
    • If cycles have GCPs, run LAS-to-Control for each.
  4. Accuracy
    • Achieves absolute georeferencing tied to real-world coordinates.
    • Typical RMSE: 1–3 cm (depending on GNSS quality and GCP distribution).

Pros:

  • Survey-grade accuracy.
  • Easier QA/QC.
  • Ideal for BIM, construction, and compliance projects.

Cons:

  • Requires field time for GCP setup.
  • More complex workflow.

✅ Workflow without GCPs

Best for: Relative alignment when absolute accuracy is less critical.

Steps:

  1. Base Cycle
    • Use RTK or PPK Cycle as reference (GNSS only).
    • No surveyed GCPs, so accuracy depends on GNSS solution.
  2. Indoor Cycles
    • Register using cloud-to-cloud alignment (ICP).
    • Use tie points captured with TVGO for better rigidity.
    • Minimum: 3–5 tie points per cycle, well distributed.
  3. Accuracy
    • Relative alignment is good, but absolute accuracy limited by GNSS drift.
    • Typical RMSE: 3–10 cm (or more indoors).

Pros:

  • Faster field workflow.
  • No need for surveying equipment.

Cons:

  • Cannot guarantee survey-grade accuracy.
  • Drift accumulates if overlap is poor.

Key Differences

Aspect With GCPs Without GCPs
Absolute Accuracy High (1–3 cm) Moderate (3–10 cm)
Field Effort Higher (survey GCPs) Lower
Registration Method LAS-to-Control + ICP ICP + Tie Points
QA/QC Easier (RMSE vs GCPs) Visual + RMSE only

 

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