At the recent Geo Week event held in Denver Colorado, I had the opportunity to speak with numerous LiDAR sensor manufacturers. One conversation thread that stood out was the absence of a 7-parameter Helmert transformation in many LiDAR systems. While this feature is vital for transforming raw data collected in the WGS84 coordinate system to a ground-based coordinate system, it’s surprising how many manufacturers still haven’t included it in their OEM software that transforms proprietary collection data to a standard point cloud LAS file.
For those unfamiliar, the 7-parameter Helmert transformation is a mathematical method used to convert geographic data from one coordinate system to another. It’s especially important for ensuring LiDAR data collected in geodetic coordinates is aligned with local project coordinate systems—critical for accurate land survey deliverables. The challenge is that not every region works in State Plane Coordinate Systems (SPCS), and in mountain states like Colorado, where local projections and geographies are more complex, having a robust coordinate transformation built into the software is essential.
Aligning Point Clouds to Targets: A Repetitive Challenge
I’ve seen firsthand how many data providers still rely on aligning and scaling point clouds to physical control targets, a method that comes with significant challenges. One of the main difficulties lies in the precision required to hit a point dead center on each control target—a feat that’s often more idealistic than realistic. The inherent variability in targeting can lead to inconsistencies, especially when these targets are not perfectly defined in the field or during subsequent scans.
This brings us to another challenge: repeatability. Without an integrated 7-parameter transformation, aligning point clouds across multiple flights—whether on the same job site or adjacent ones—can become problematic. Each time you attempt to align the data, you risk slight variations that can accumulate over time, leading to discrepancies. For ongoing projects with frequent flights, ensuring data is scaled and aligned consistently is crucial for maintaining accuracy.
Steps in the Right Direction
At TopoMatters®, we’ve been using DJI Terra (and their L2 sensor, which with our TopoPro® post-processing, punches WAY above its weight class), to perform our 7-parameter transformations. This feature has significantly improved our workflows, ensuring the raw data comes out of the system correctly ground-scaled to the project coordinate system. That said, we also work with datasets captured from other LiDAR systems and, in those cases, still encounter the same issues that our survey clients have complained about in the past: data in state plane, and the headache, extra cost, and specialized knowledge (that may or may not be readily available) required to correctly scale it.
The value of integrated transformation tools is clear. When a LiDAR sensor system can automatically output data in a local coordinate system, it eliminates much of the manual intervention and potential for error in the post-processing phase. For those of us working with multiple flight sessions or job sites, this capability ensures our data is consistent, repeatable, and—most importantly—accurate.
Looking Ahead: A Collaborative Approach
The industry as a whole would benefit from greater integration of 7-parameter transformations in LiDAR OEM software. This isn’t just a matter of convenience but of efficiency and precision. Ideally, data would be consistently aligned from the moment it’s captured, providing seamless workflows for surveyors (and subcontractors like TopoMatters) and minimizing the time spent correcting errors.
That said, this is a gradual process, and I recognize that many companies are working hard to enhance their hardware, software, and workflows. I encourage others in the LiDAR community to share their experiences—whether they’ve encountered the same challenges, found other solutions, or are considering similar advancements in their own software. Ultimately, the goal is to ensure that we, as professionals, can consistently produce high-quality, reliable survey deliverables. With continued collaboration and shared insights, we can work toward that future.
Brian Yohn
Founder, TopoMatters
I’d love to hear from others in the LiDAR community about how you’re tackling these or other post-processing challenges, or any solutions you’ve found. Let’s keep the conversation going and continue pushing for better, more efficient workflows that can elevate the quality of our deliverables.
You can reach me at [email protected].
