Feature
Vercator SLAMs the cloud
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aser scanning is rapidly becoming a commonly used technology within the AEC sector. While it has traditionally been the realm of surveyors, smaller and lower cost devices have now started to drive wider adoption. Innovations such as SLAM technology (Simultaneous Location and Mapping) liberate scanning devices from tripods, to enable real-time data capture on the move. This rapidly speeds up capture, lowering the cost, but sacrifices some degree of accuracy. With portable scanning becoming a common practice, users are starting to mix-and-match scanning technology on the same projects. SLAM is also set to benefit from the huge amounts spent by automotive firms, developing automated driving systems and in aerospace for unmanned UAVs, where it provides mission critical ‘vision’. Last year London-based Correvate launched its Vercator service, a cloud platform that automatically registers static-captured, scanned data from a series of overlapping scans. The registration engine, which uses technology developed by University College London’s Department of Electronic and Electrical Engineering, finds multiple features within each scan and auto-aligns the huge data assets. Correvate estimates its cloud service is 60-80% faster than manual alignment and has a simple token-based system for processing data. You can read more about it in this AEC article from April 2020 (tinyurl.com/correvate)
SLAM dunk Correvate has now incorporated SLAM into its service, so AEC Magazine caught up with Correvate’s Charlie Cropp, a laser scan industry veteran, to find out more. 32
January / February 2021
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UK start-up Correvate offers cloud-based registration for laser-scanned point clouds. With a growing trend to mix and match scanning technologies, the service recently added support for SLAM, terrestrial LiDAR and UAV captured data. Martyn Day
“The first thing to say is that we’re not solving the SLAM algorithm. We’re leaving that to the likes of GeoSLAM, Paracosm and NavVis,” explains Cropp. “We’re just taking their processed point cloud, once it’s been captured and solved, and uploading the point cloud into our system to then align with other scanned data. “We’ve seen a couple of big shift changes. The first is that people are deploying different arsenal. Firms who’ve traditionally always had a Leica or a Faro static scanner, are starting to run with GeoSLAM or Paracosm scanners and doing data capture to capture different levels of detail, different accuracy, tolerances, to match different project requirements. “There is a realisation that they don’t need to be sending £50/60/70k pieces of kit to site when they could do so with a £15,000 to £20,000 solution. So that’s been a shift change in how the industry is working with SLAM data. “Secondly, we are also seeing people actually use these devices in the same way they would a traditional scanner. So instead of having long, large linear scans that potentially have drift, they are truncating their capture and keeping that data section quite small. “The challenge with doing that is they end up with lots of small sections — chunks of scan data which their hardware provider’s software can’t actually manage. They then have to look for an alternative software programme for
processing, such as CloudCompare, which is less than ideal. “We’re seeing firms producing ‘hybrid’ datasets; capturing core areas with a higher accuracy scanners and filling in other areas with a GeoSLAM (or equivalent SLAM) scanner. “We get a lot of people coming to us with mobile data or the SLAM data captured from drones, handheld scanners, and they’re wanting to align them all together. Our core algorithm initially came out of the static scan world where the centre point, the zero point, was in the centre of the scan. “With SLAM that centre point can be outside of the scan data and we would typically see that data fail. We’ve been able to enhance our algorithm and go from a 0% success rate to 100% success rate, enabling automated registration for SLAM data as well as static data.”
Data trade-offs SLAM has always been seen as ‘dirty’ or ‘noisy’ data as it’s far from clean and usually not colourised. Cropp shared her thoughts, “Laser scanning has always been a compromise. There’s always been trade-offs. Do you want data quality? Do you want speed of capture? How much data do you want? How much can you work with? And those three key elements are still relevant today. “Looking at a GeoSLAM scanner,
The NavVis VLX wearable mobile mapping system
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27/01/2021 12:45
