Join this webinar with Jon Romero, owner of Scout Aerial Solutions, to learn about the benefits of versatile mapping solutions and how small businesses can use innovation for long-term success and cost-effectiveness. Jon is an advocate of implementing new technologies to suit his growing business and the needs of his clients, for long term and cost effective success.
Join us, and guest speakers Argenis Pelayo and Ana Gutierrez from JP Global Digital to discuss the key to unlocking the potential of reality capture data with GeoSLAM and JP Interactive Viewer (JPIV).
JPIV is a cloud-based software for efficiently managing, visualising, and sharing reality capture data with your teams and clients. We’re going to show you how you can leverage your GeoSLAM data by integrating JP Interactive Viewer into your workflows.
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Participe deste webinar para aprender sobre o uso de escaneamento LiDAR portátil e da tecnologia SLAM para criação de modelos 3D em minutos.
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Watch this webinar to understand the many versatile ways that you can collect your point cloud data, whatever your environment.
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Watch this webinar to learn all about the new features of Connect 2.3.
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This webinar discusses how handheld LiDAR can be used within commercial and residential real estate.
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This webinar discusses how to integrate GeoSLAM solutions as part of data capture in forest environments.
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Watch this webinar to learn how you can utilize mobile laser scanning data for the AEC industry
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Watch this webinar to learn all about the new features of Connect 2.2, as well as a first look at GeoSLAM data in online data viewers for easy sharing and collaboration.
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How precise and accurate are the 3D models you can make with the Elios 3?
Watch this webinar to learn the results from recent testing we did with our partners, Flyability, to assess the accuracy and precision of 3D models made using LiDAR data collected by the Elios 3 and processed on GeoSLAM Connect.
In the webinar we went over the results of the testing and explain how to use GeoSLAM Connect to make survey-grade 3D models with the Elios 3.
Watch this webinar to learn how educational institutions are inspiring the next generation of surveyors using GeoSLAM handheld LiDAR scanners.
Hear from three guest speakers from different Universities and Colleges across the world discussing their own individual experiences uses handheld LiDAR scanners to support education and inspire their students.
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Monitoring construction progress comes with many challenges and we’d like to help you solve them with SLAM mapping. Watch this webinar to learn how to track the progress of small and large construction projects using mobile LiDAR and automated analytics.
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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.
