FARO Connect 2023 Overview

The Reprocess SLAM Workflow allows you to define advanced processing parameters to help processing in more challenging capture environments. Typically, you already have processed data using the different preset capture environments, available in the default workflow at project creation. If this has not provided an acceptable outcome, this workflow allows you to modify parameters before reprocessing SLAM.

The alignment workflow allows you to align multiple point clouds by providing several tools to manually select and match common user selected points between scans.

A fine alignment process is also available, and this requires that all required scans are closely aligned at the start of the process.

Note: When capturing the data, it is essential that there is at least 30% overlap between captures.

There are 5 Registration workflow which allow you to easily align two datasets together, by either:

• using common extracted reflective targets or user measured reference targets, present in both scans, or
• using extracted reflective targets or user measured reference targets together with the imported georeferenced Control Points of the captured reflective or reference targets.

The Reflective Target Alignment, Reflective Target Georeferencing, and Stop and Go Alignment workflows, use a rigid transformation, meaning the point cloud is transformed using a rigid translation and rotation matrix to best fit the control points from the reference scan.

The Stop and Go Georeferencing workflow can use either a rigid transformation, or non-rigid transformation, meaning the the original data is reprocessed using the SLAM algorithm to ensure that the trajectory matches the control points.

There are 3 analysis workflows that can be applied to project dataset, depending upon you output needs.

• Change Detection

When a project has scans (.laz) of the same location, captured at different points in time, using the Change Detection workflow, the point clouds can be compared to establish differences / changes. The output point cloud colorizes objects or surfaces to indicate the changes that have occurred during the period between scans.

• Floorslices

Allows the user to create a scaled PNG of a slice through the point cloud at a set height above the floor.

• Static Scans with Flash

This workflow analyses Orbis scans and automatically generate static scans at the positions where the person capturing the scan stopped. The generated scans are enhanced with Flash and color data.

After you have processed data and confirmed the output, you can apply additional filters to further thin and clean the data. For this purpose, the following workflows are available:

• Noise

With this filter you can improve the point cloud by removing outlying data points on planar surfaces. Therefore, it is best suited to environments with planar surfaces, e.g., walls, both indoor data and for urban environments with strong planar features.

• Outlier

With this filter you can remove outlying data points from the point cloud. Outlying points can be caused by rain or dust or due to partial reflections from edges.

• Range

With this filter, you can remove less dense and lower accuracy points that exist furthest from the scanner. The points are filtered with respect to their distance from the trajectory.

• Thinning

This filter creates a uniform point cloud and reduces the number of points to reduce the thickness of scanned surfaces, based upon either a 2D or 3D grid method defined by the user.

• Transient

This filter is useful to identify temporary points, caused by people or vehicles that are moving past the scanner during acquisition.

Example: Transient Filter

Users can post process images captured during scanning after importing, using Vision Tools:

• Stitch Vision Images produces a set of panoramic images created from the images acquired by the camera during the scanning process. The workflow outputs a series of images, depending on the user selection plus an image timing file which is used when calculating the camera positions.
• Calculate Vision Camera Positions computes the exact position and orientation (Pose) of the camera when the images were taken, which is required to align the point cloud data and the images. This workflow outputs a .gs-vision file.
• Colorize Cloud Using Vision extracts the color information from the equirectangular images and applies it the to the point cloud data, outputting a colorized .laz file with RGB information.

Users can export project artifacts to various formats, allowing them to be imported into other applications, such as FARO SCENE:

• Point cloud .laz can be exported to .e57, .las, .laz, .ply or .txt
• Control Points / Reference Points can be exported to .txt
• Static Scans can be exported to .e57 structured, which can then be imported and analyzed in FARO SCENE.