Jim Foster has worked in the surveying industry for over 18 years, building a well-respected company with high profile clients from all over the United States. Originally the owner of a product design company, Jim saw the opportunity to begin a new adventure in the world of surveying and has never looked back.
In this Q&A, we speak with Boaz Teixeira, laser scanner specialist at CPE Tecnologia, our Brazil dealer, about how the geospatial market in Brazil is developing and their involvement in one of our recent projects, a scan of Rio de Janeiro’s Christ the Redeemer.
In 2020, Professor Mona Hess, head of ‘Digital Technologies in Heritage Conservation’ at… Read More »Creating a digital twin of Cistercian landscapes.
Gabriel de Praxedes, our Latin America channel manager, undertook the project of… Read More »Q&A with Gabriel: Getting the inside scoop on Christ the Redeemer.
Standing proudly atop Corcovado Mountain and overlooking the beautiful city of Rio… Read More »Christ the Redeemer, as you’ve never seen it before.
This year we launched some amazing new products, delivered the ‘Adjust to… Read More »GeoSLAM 2020 Roundup.
When Visualskies needed to find a way to unearth ancient cities now… Read More »The ZEB Horizon unearths a city lost to jungle.
Osvaldo Neira is a Mining Applications Engineer at GEOCOM, a GeoSLAM dealer… Read More »Q&A with Osvaldo Neira of GeoSLAM Dealer GEOCOM
Laying high speed internet cables with GeoSLAM with UTB and A1 Telekom… Read More »Laying high speed internet cables with GeoSLAM
GeoSLAM has partnered with Drone specialist HELIGUY.com™ to provide professionals with sophisticated… Read More »New UK partnership with drone specialists Heliguy
GeoSLAM’s Big tour of Canada Channel Manager for Canada, Matt Teppler (based… Read More »GeoSLAM’s Big tour of Canada
Bat Conservation International work around the world to conserve different species of… Read More »ZEB Horizon helps Bat Conservation International
Known control points are captured during a scan and automatically compared and matched to the associated coordinates during the processing stage in Connect. A rigid and/or a non-rigid adjustment can be made to the dataset and an accuracy report is exported, highlighting how successful the transformation was. Users can now view and manipulate the processing parameters to ensure a more accurate match between points.
Align multiple scans using a combination of manual and automatic processes. This workflow can be performed on two or more scans in the same project. Users have a choice to export the aligned scans separately or as a single merged point cloud.
Common data capture scenarios, such as UAV, outdoor, indoor, linear, and vehicle, have been characterised in Connect and data processing pre-sets for each environment have been defined. These can be selected at the beginning of the data processing stage allowing this process to be highly simplified.
Both methods match the scan data from a ZEB Locate system with the GPS data collected from the antenna to georeference the point cloud. When a scan starts and ends in the same place, this is classed as “closed loop”. “Open loop” is when the start and end position of a scan are in different locations. Standard SLAM practices apply to both methods of data collection.
Open Loop SLAM for the ZEB Locate is available on request – let’s talk about it.
Common static points are captured during several scans meaning that these datasets can be automatically aligned. A single point cloud is then exported as if the data was captured in a single scan.
Horizontal and vertical slices can be taken from any location within the point cloud. Horizontal floor slices can also be automatically taken at a given height above the floor as defined in the processing stage.
Mostly used in the construction industry, multiple point clouds can be compared and any areas that have changed are automatically highlighted. Point clouds can also be compared with CAD models – for instance to track progress on a construction site – and PDF reports can be generated to present this information.
Some SLAM software algorithms have been made available as open-source on the internet, but they are purely algorithms and not a product that you can take and use off-the-shelf. SLAM is most successful when it is tightly coupled and designed with specific hardware in mind. A generic SLAM cannot perform as well as one that has been specifically designed for a purpose.
Visual SLAM is closer to the way humans navigate the world, which is why it’s popular with robotic navigation. But in the same vein, vSLAM will have the same image-capture challenges as humans do, for example not being able to look into direct sunlight, or not having enough contrast between the objects picked up in the image. These can be overcome indoors, however, you may need to map a forest, tunnel or urban canyon. While SLAM technologies don’t rely on remote data (meaning you can scan areas where there is no GPS), you do need to ensure the SLAM technology you chose operate well inside, outside, in daylight and darkness.
Mapping a property is time-critical. Ideally, you want to make a single visit and gather sufficient data to create a highly accurate 3D model. Ensure the software you choose transforms 3D point cloud data into actionable information in real-time. This allows you to view and interrogate your data whilst still in the field, and make any adjustments, or collect missed data, then and there.
If you’re trying to map an enclosed environment (e.g. tunnel, mine) or a complex, difficult-to-access space such as a heritage building with tight stairwells and uneven floors, you need to use fully-mobile, adaptable technology. Wheel-based systems, often used with the vSLAM camera, will struggle with access. Handheld devices or LiDAR scanners that can be attached to a drone or pole and still deliver accurate results in a rugged environment are best for navigating hazardous spaces.
While vSLAM is able to provide a qualitative high-level map and sense of the surrounding features, if you’re needing survey-quality accuracy and rich-feature tracking at a local level, you’ll need to consider LiDAR. Cameras require a high-frame-rate and high processing to reconcile data sources and a potential error in visual SLAM is reprojection error, which is the difference between the perceived location of each setpoint
and the actual setpoint.
In order to deliver the depth required for high-quality data, a number of depth-sensing cameras are needed with a strong field of view. In most cases, this isn’t possible, especially as cameras with high processing capabilities typically require larger batteries which weigh down airborne scanners, or limit the time of flight. LiDAR is both faster and more accurate than vSLAM, and can deliver detailed point clouds without expensive (and timely) camera processing.
