Autonomous scanning technology has unlocked great efficiencies on construction sites since its inception. It provides project managers with precise and detailed data illustrating exactly where everything is at that moment in time, and its precise measurements. This kind of data has been critical for avoiding clashes in structural overlaps on existing mechanical, electrical, and plumbing systems, ensuring that new design fits well without being destructive. But when that data isn’t updated on a regular basis, it can do more harm than good.
To learn more about just how much value autonomous simultaneous location and mapping (SLAM) scanning brings to construction sites, Exyn Technologies and Keystone Precision Solutions are teaming up to deliver a webinar entitled Autonomous Mapping Robots can Reduce Construction Work on Wednesday July 1, at 1:00 PM Eastern, hosted on Commercial UAV News
Matt Malencia, senior technical project manager at Exyn Technologies and Kenneth J. Fronheiser, PLS, director of strategic integration and technology at Keystone Precision Solutions, will be speaking in this 60-minute session, covering three key considerations when it comes to mobile SLAM scanning.
Mobile SLAM Complements Total Stations Rather than Replacing Them
The two tools solve different problems, and the best workflows use both. A total station delivers survey-grade, point-by-point accuracy, making it the right tool for setting control, laying out critical points, and verifying the measurements that absolutely must be correct. Mobile SLAM, by contrast, captures a dense point cloud of an entire space in minutes while an operator simply walks or flies through it. They provide enormous coverage and speed, but with more relative drift than a total station's discrete measurements.
The two reinforce each other. Crews use total station control points to georeference and constrain the SLAM data, anchoring that fast, dense capture to true project coordinates and correcting for drift. The result is the best of both worlds: total-station accuracy where it matters, with SLAM filling in complete as-built context everywhere else.
Onsite Post Processing
When crews process on-site, they can validate data quality while there's still time to act on it. If a scan has a gap because a door was closed, or a registration has an error because of a reflective surface, the team knows before leaving the floor and can re-fly or fill the gap. If they chose to process back at the office two days later, on the other hand, that floor may already be closed in or access may be gone, permanently losing the data capture opportunity.
Scanning Cadence
The presenters say the cost of poor scan cadence almost always spikes when trades start stacking. Early in a project there's enough margin and visibility that gaps are forgiving, but the moment multiple trades work concurrently in the same space, the cost of a bad or outdated model compounds quickly.
A construction site is one of those places where time truly is money. Decisions are made daily by people who can't afford to wait for updated data - foremen coordinating sequencing, supervisors managing material staging, project managers tracking against schedule. If the model they're referencing is three weeks old during that phase, they're essentially operating on memory and assumption, and every trade that touches an area based on a stale model is a potential rework event.
Attendees will walk away with a practical framework for evaluating their current reality capture workflow and identifying where delays in data collection may be creating risk. They'll see how autonomous mapping can help construction teams capture site conditions more frequently, improve coordination between trades, and make faster decisions based on current field conditions rather than outdated information.
Most importantly, they'll understand how more timely reality capture can help reduce rework, improve project visibility, and keep projects moving forward with confidence.
Not able to make it to the live webinar? Register anyway and we will send you the recording.
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