About
Maximize the geometric accuracy of your scan data with our powerful automatic point cloud adjustment software. Developed for airborne and UAV LiDAR, also supports topo-bathymetric and sonar data.
Perform strip adjustment faster and more accurately compared to existing software, thanks to an advanced registration algorithm, high frequency IMU/GPS corrections and flexible sensor models. Correct very large datasets, complex flight geometry and difficult issues. No need to filter the data, as the approach is robust to vegetation, buildings and noise.
Many options: control points, QA/QC images and reports, boresight calibration, trajectory cutting, blending, split and merge...
Multi-platform (including cloud), flexible floating licenses, no hidden dependencies, simple and powerful command line interface.
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Before/After StripAlign relative accuracy
Vertical difference between overlapping strips. Left: original data. Right: geometric corrections applied (high frequency drifts, calibration). The point cloud registration is robust to vegetation, mismatched surfaces and natural differences between points of view. Optimal corrections are computed automatically from relative displacements using a rigorous model and Bayesian inference.
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Automatic boresight calibration
Vertical differences between two overlapping cross-strips, before (left) and after (right) calibration using n strips simultaneously. A standard calibration cross, or any set of overlapping lines can be used, not necessarily parallel or perpendicular. While boresight can account for a significant part of geometric errors, it is not sufficient to compensate time-dependent IMU errors, this is why we developed HF drift corrections.
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Relative accuracy improvement
From top to bottom: z-difference histograms for original uncorrected data, calibrated data, and fully corrected with all components enabled - calibration, distortions and HF attitude and position adjustments.
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Roughness and hill shade maps
StripAlign can export many useful QA/QC images, rasters and reports. Here are examples of roughness (left) and hill shade (right).
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Point cloud alignment with dense vegetation
Example of difficult dataset with significant vegetation (Muir Woods, California, collected by GEO1). Extraction with WavEx and alignment with StripAlign. The registration algorithm found some sparse hard surfaces to estimate relative displacements, so the strip adjustment could be completed successfully despite the vegetation cover.
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