2.1 SLAM vs. Static Scanning

How Traditional Static Scanning Works

Traditional terrestrial laser scanning (TLS) uses a scanner mounted on a tripod in a fixed position. The scanner rotates and fires laser pulses in all directions, capturing a dense sphere of point data around that single point. When coverage of a large space is needed, the scanner is moved to a new position and the process repeats. Every position becomes a separate scan.

The scans are then registered together in post-processing by identifying common reference targets or overlapping geometry between adjacent setups. The quality of the final dataset depends largely on the density of scan positions and the accuracy of the registration between them.

For a typical commercial building floor, a TLS workflow might involve 20 to 100 or more scan positions, each requiring the operator to stop, level the instrument, wait for the scan to complete, and move to the next position. The work is methodical and predictable. Errors tend to be localized to specific setups or specific registrations.

How XGRIDS Mobile SLAM Works

XGRIDS devices use Simultaneous Localization and Mapping, or SLAM. Instead of capturing data from fixed positions, the scanner moves continuously through the space while capturing. The system uses three data sources working together in real time: LiDAR (laser ranging), visual cameras, and an IMU (inertial measurement unit). These three sources are fused continuously to track the scanner's position as it moves and build the point cloud simultaneously.

The tradeoff is that the system's ability to stay accurately localized depends entirely on having enough environmental features to track against. When the scanner moves through a feature-poor area, a long blank corridor, a large open room with no furniture, tracking quality degrades. This is fundamentally different from TLS, where feature-poor environments slow you down but do not affect data quality.

The other key mechanism is loop closure. As drift accumulates over distance, the system detects when it has returned to a previously scanned area and uses that recognition to measure and correct the accumulated error. Without loop closure opportunities built into the scan route, drift compounds without correction. This is why route planning is the single most important field skill in mobile SLAM.

What XGRIDS Devices Capture

All three XGRIDS devices (L2 Pro, K1, and PortalCam) use the Multi-SLAM system combining LiDAR, visual cameras, and IMU. The LiDAR provides geometric measurement. The cameras provide visual tracking features and, in the case of the L2 Pro and K1, color data for the point cloud. The IMU provides continuous motion sensing between LiDAR pulses. All three operate simultaneously from the moment scanning begins.

A real-time preview of the developing point cloud is visible in LixelGO during scanning, allowing the operator to monitor coverage and identify gaps before leaving the site. This is one of the most important practical advantages of mobile SLAM: you know what you captured before you walk out the door.

How Planning Changes

The mental model for planning a mobile SLAM project is different from TLS in almost every respect.

The most consequential planning decision is the route. A well-planned SLAM route creates multiple loop closure opportunities throughout the scan. A poorly planned route, typically a linear path from one end of a building to the other, accumulates drift continuously with no correction. The difference in data quality between a good route and a bad route can be the difference between a usable dataset and an unusable one.

The second major planning difference is the coordinate strategy. In TLS, georeferencing is often applied after scanning by transforming the registered point cloud to a coordinate system using control points. In XGRIDS mobile SLAM, the georeferencing strategy must be decided before scanning begins. RTK must be configured and connected before the scan starts. GCPs must be physically placed and marked during the scan. A scan captured without georeferencing cannot be retrospectively tied to a real-world coordinate system.

How Technique Changes

In TLS, the operator's physical technique during scanning is largely irrelevant. The scanner is on a tripod. The operator waits.

In mobile SLAM, the operator is the instrument platform. How you move, how fast, how you hold the device, and where you direct it all affect the quality of the data. This is the biggest adjustment for anyone coming from a TLS background.

The Key Technique Requirements

None of these requirements exist in TLS. They are not difficult, but they require building new habits before working on a project where a failed scan has real cost.

A Note for Experienced TLS Users

If you are coming from a TLS background, you already understand coordinate systems, registration, accuracy specifications, and delivery workflows. That knowledge transfers. What requires deliberate adjustment is the error model and the field execution approach.

The practical implication: experienced TLS operators sometimes scan too quickly on their first XGRIDS project because they are accustomed to a workflow where speed in the field has no quality cost. With mobile SLAM, moving through a building faster than the system can reliably track produces drift and gaps. The same operator who runs 60 TLS setups in a day needs to adjust expectations for how quickly a SLAM scan route can be executed.

The other adjustment is acceptance of a pre-scan planning step that TLS does not require. Choosing your route, identifying loop closure opportunities, and deciding your coordinate strategy before arriving on site is not overhead, it is the quality control step for mobile SLAM projects.