Between tours and filming, Old Tucson Studios is a dynamic environment that… Read More »GeoSLAM goes wild wild West
Taylor Handschuh is a LiDAR and 3D scanning expert known in Tucson,… Read More »Tips and tricks – ZEB Cam
Leverage your GeoSLAM data by integrating JP Interactive Viewer into your workflows. JPIV allows you to unlock the full potential of your reality capture data and distribute actionable insights across your teams.
Click here to learn more about GeoSLAM and JP Interactive Viewer
Our support team will be available for GeoSLAM Care customers on:
Click here to view the release notes.
Autodesk Recap contains tools for the manipulation and interpretation of high quality point cloud data and to aid designers and engineers in their creation of 3D models for real-world projects and assets (e.g. buildings and other infrastructure). It’s integrated design features help to streamline workflows, for example Scan to BIM. Recap is used to create initial design projects that users can then take into other Autodesk modules (e.g. Revit, Navisworks, AutoCAD).
Autodesk Navisworks is a comprehensive project review solution that supports co-ordination, analysis and communication of design intent and constructability. The software can be used as a common data environment (CDM) for multidisciplinary design data created in a broad range of Building Information Modelling (BIM) packages. Using the tools within Navisworks, users can anticipate and minimise and potential problems between the physical building and the structural model.
Autodesk Revit is a building information modelling (BIM) software. It contains tools which allows for planning and tracking throughout the building’s lifecycle. The software also allows multiple disciplines to collaborate more efficiently and make more informed decisions early in the design process. As GeoSLAM’s hardware allows for quick data capture, the equipment can be used to scan any existing buildings with the purpose of using the data to produce a digital twin.
Click here to learn more about GeoSLAM and Revit
Orbit GT allows users to capture and manage available 3D data (LiDAR data and imagery), extract a range of features for map production and make data sharable. All Orbit modules are ready to be used with 3D data from indoor, oblique, UAS and mobile mapping projects with other extensions that can be added to the Publisher and Orbit Cloud. Orbit can be used with the ZEB Discovery solution.
ContextCapture is a reality modelling tool, allowing for the import of any point cloud and imagery data for the creation of high resolution reality meshes. These realistic meshes are accurate representation in 3D with high resolution RGB values of any scanned environment. By using GeoSLAM data in ContextCapture the users are able to create indoor reality meshes, which has been never possible before.
Microstation is a 2D/3D software for designing building and infrastructure projects. It includes building information modelling (BIM) tools to document and assess any type of asset throughout its lifecycle. GeoSLAM solutions are often used in Microstation in the underground mining sector and to assess the current stage of any built environment, update the design model, and generate BIM information.
With the GeoSLAM Connect stop-and-go georeferencing feature, users can easily georeference headings from known positions and map for analysis of overbreak, underbreak, undercutting and blast roughness calculations. This information is used within Deswik Mapping to analyse headings and levels.
Outputs from GeoSLAM’s solutions can be input to Esri’s GIS programs and apps, including ArcPro, ArcDesktop, ArcGIS Online and ArcScene. Join point clouds with local geodata or classify and edit scans based on their geography and statistics.
GeoSLAM are proud to be silver partners of Esri.
Click here to learn more about GeoSLAM and Esri
Micromine is a detailed and diverse mining software that provides solutions including modelling, estimation, design, optimisation and scheduling. Once data is exported from Connect it can be imported into Micromine and easily converted into wireframes. These can be used in Micromine for further studies into volumetric slicing, over and underbreak analysis, geologic modelling, face mapping and many more.
Click here to learn more about GeoSLAM and Micromine
Terrasolid provides tools for data processing of airborne and mobile mapping LiDAR data and imagery. It includes different modules for tasks like data manipulation, calibration, georeferencing, point cloud classification, modelling and many more. It is a very powerful tool for a variety of industries, surveyors, civil engineers, planners, designers. Full, UAV or lite versions of Terrasolid modules are available for both MicroStation or Spatix software. All GeoSLAM products are compatible with Terrasolid and GeoSLAM data can be enhanced and edited with this software.
Floorplanner allows you to draw accurate 2D floorplans within minutes and decorate with over 150,000 items from kitchen appliances to tables and chairs. Data is exported from GeoSLAM Connect in PNG file format with a scale of 1cm per 1 pixel and can be taken into Floorplanner.
GeoSLAM 3D point cloud data can be imported into Unity 3D Game Engine to generate interactive 3D scenes, where users can create 3D BIM models with textures and explore the space in 3D photorealistic environments.
Although Unreal Engine is mainly built for developing games, increasingly users are starting to use it to develop VR applications for understanding the current conditions of buildings, infrastructure and similar. Unreal Engine with a point cloud plugin can be used to visualise GeoSLAM point clouds in VR, which allows for collaboration, simulation and the understanding of current conditions of any scanned environment. Additionally, Unreal Engine tools are completely free.
Arena4D is a software package for marking up, annotating and editing 3D point cloud data containing a various export capabilities. It has a powerful and simple to use animation package which allows users to visualise massive point clouds in a simple way. GeoSLAM data can be simply uploaded and used in this package for the assessment of the current conditions of any structure, comparing differences between captured data (as built) to designed model (as designed).
Pointfuse generates 3D meshes from point cloud data and classifies them to building ceilings, walls, windows and other features in IFC format. By using GeoSLAM data with Pointfuse users can very quickly create a classified BIM model with minimal manual input or expertise needed.
MineRP has a Spatial DB that uses GeoSLAM data to represent visually the real environment of the underground mine. The software uses other data layers to overlay information on the digital landscape for decision making and tracking.
Pointerra provides a powerful cloud based solution for managing, visualising, working in, analysing, using and sharing massive 3D point clouds and datasets. Pointerra allows users to simply visualise and interrogate GeoSLAM data from anywhere.
Nubigon is a software solution that allows users to seamlessly interact with large point clouds and create visualisations and animations. Take your GeoSLAM point cloud data into Nubigon to create eye-catching flythrough videos.
Here is an example of a visualisation created in Nubigon using GeoSLAM point cloud data:
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.
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.
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.
Import multiple .geoslam files into Connect for processing and the scans will be processed in the order they were imported. The size of the queue can be defined by the user.
Export your point cloud into a range of formats, including LAS, PLY and TXT. Datasets can now also be exported as structured or unstructured E57 files, both of which include embedded panoramic images.
UK payment plan info
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.