What is StripAlign ?

StripAlign is an automatic strip alignment tool: it registers overlapping LiDAR swaths and corrects both relative and absolutegeometric errors. It allows to effectively reduce discrepancies between strips due to IMU attitude and position errors, and tocombine corrections optimally in order to minimize both relative and absolute error. It uses a rigorous time-dependent approachto address effects such as IMU drifts and oscillations, which cannot be corrected with classical sensor calibration.

The registration algorithm takes advantage of all the last returns, filters out outliers on the fly (including vegetation, mismatchedbuilding walls, missing and bad data). LAS ground classification can be used if available but is not necessary. A roughnessthreshold can also be used to exclude vegetation and speed up the computation. Water bodies can be filtered based onheight. No planes, or geometric objects of any type are required: natural surfaces work just as well as built-up areas.

It is possible to set some of the strips as references to keep them fixed, when they are part of an already aligned dataset, whichsaves time. No control data is required, but if available they can be used for QC and vertical bias compensation.

What it Does ?

Key Functionalities of BayesStripAlign

1. Automatic Strip Alignment

• BayesStripAlign corrects both relative and absolute errors between overlapping LiDAR swaths.
• Its algorithm takes a time-dependent approach to adjust for IMU drifts, GPS inaccuracies, and other distortions.
• The tool effectively combines data corrections to minimize residual errors, which is especially beneficial for large-scale mapping projects.

1. Calibration and Correction Capabilities

• The software automatically computes and corrects systematic geometric errors, such as internal sensor misalignments, boresight misalignment, and lever-arm discrepancies.
• It allows full calibration using cross-flights or at least three parallel lines, calculating external and internal parameters to optimize correction precision.

1. High-Frequency (HF) IMU Drift Compensation

• BayesStripAlign provides HF drift corrections to refine trajectory and attitude data, enabling each LiDAR swath to be adjusted in real time.
• Dense point cloud matching is used to create a 1 Hz (or faster) correction cycle, minimizing IMU drift across the flight path.

1. Multichannel and Multi-Sensor Compatibility

• The software is compatible with multichannel scanners, such as forward/backward looking sensors (e.g., Riegl Q-1560).
• New feature: Version updates include improved multichannel corrections, allowing seamless calibration of sensors across different channels.

1. Quality Control (QC) and Mapping

• BayesStripAlign provides various tools for assessing the alignment quality, including Z-difference maps, height maps, and point density maps.
• For improved visualization, grayscale TIFF or color JPEG maps are generated, which help identify discrepancies, vegetation, or any misaligned features.
• New Updates: Recent updates allow generation of group overlap maps, fixed/mobile overlap rasters, and robust time deviation analysis to improve QC.

1. Command-Line Interface with Batch Processing

• BayesStripAlign operates via command-line interface, allowing for batch processing and integration with third-party workflows.
• Complex processing tasks can be simplified using single commands, with multiple options configured through a persistent option file or command scripts.

Notable New Features (Latest Versions)

• Improved Multichannel Alignment: Increased accuracy in multichannel corrections with refined time interval adjustments and better compatibility for multibeam UAV scanners.
• Expanded Projection and Geoid Support: Added support for Lambert Conformal Conic, Albers Equal Area Conic projections, and multi-country geoid models.
• New GCP-Based Vertical Corrections: Absolute control enhanced with GCP vertical bias correction, particularly useful for large-scale and corridor mapping.
• High Precision Mode: Updated internal drift and inversion algorithms for complex datasets, such as corridor projects or those with high overlap rates.
• PulseWave Integration: New integration for PulseWave formats, allowing alignment corrections for sonar and other pulse-based LiDAR data.