Location: Victoria, Australia
ZEB Scanner: ZEB Horizon
Scan time: 10 Minutes (via car)
This data was captured using the ZEB Horizon and car mount. The point cloud visually shows clash detection between the powerlines and the overhanging trees.
Would you like to see a specific dataset that’s not on this page? Contact [email protected]
Location: Sydney, Australia
ZEB Scanner: ZEB Horizon
Scan time: 5 Minutes
This data was captured using the ZEB Horizon in just 5 minutes and consists of 7.7 million points.
Would you like to see a specific dataset that’s not on this page? Contact [email protected]
Location: Johor Bahru, Malaysia
ZEB Scanner: ZEB Horizon and ZEB Vision
Scan time: 5 Minutes
See the data flythrough video here.
Would you like to see a specific dataset that’s not on this page? Contact [email protected]
Bengaluru, India
25-27 minutes per scan
40 acres
Informal Settlements
Surveying
The informal settlements in Bengaluru, India, house roughly 16% of the city’s population and there are around 500 recognised in this area.
Currently, Bengaluru is going through a period of modernisation and urbanisation which has caused the city limits to expand. As a result, the local government must provide documents of every house, detailing accurate measurements of its structure, such as boundary lines and roof heights.
The government has plans to formally declare ownership of the settlements to the people living in them, which means a map of the whole area was needed.
A team from a reputed geospatial company appointed by government, surveyed the area and collected this data. This involved mapping the informal settlements in Bengaluru with their complex layouts. The task was challenging as they include many narrow lanes that are difficult to access. Additionally, people were going about their daily activities.
Furthermore, some parts of the settlements are in dark and cramped areas whereas others are in direct sunlight. Consequently, the team needed to find adaptable solutions and technology that could handle these difficult environments, as well as deliver on the task in hand.
The area in question is a no-fly zone, which meant that drones were not an option. However, other methods for capturing data such as static scanning wouldn’t be feasible because of the busyness of the area. The cramped streets also meant the team would struggle to use a backpack solution either.
A fast and effective way to map the informal settlements was to walk through the complex passages, and a handheld laser scanner was the most suitable option. The geospatial company chose GeoSLAM’s ZEB Horizon scanner, due to its quick method of capturing accurate data and ease of use. The lightweight solution means that only one person is required to scan an area at any one time. This is less disruptive to the surveying team, which in turn is cost effective for them and their client.
The extensive maze of restricted passages and dead ends did not affect the versatile SLAM technology. By using the ZEB Horizon, the team were able to scan 40-45 different areas of the settlements. The team captured smaller areas of the informal settlements in a single scan ranging from 25-27 minutes. The team mapped larger areas over multiple scans, sending them to the client individually.
The ZEB Horizon provided good quality data and allowed us to scan difficult to access areas accurately and efficiently.
The final scans were imported into GeoSLAM Draw where orthophotos were automatically created. As a result, the engineers could make accurate measurements in a timely manner. In addition, the point clouds were exported to Terra Solid, where further information was extracted for the final report.
The final data delivered on their client’s accuracy goals. They were able to smoothly extract the boundaries and roofs of every single house in the informal settlements.
This is not the first time that GeoSLAM technology has been used to map informal settlements in India. The ZEB Revo was used to accurately scan the settlements of Mumbai in 2017. The resulting 3D point cloud helped to extract information about the elevations and sections of each house frontage.
If you’d like to learn more about how GeoSLAM solutions can help you, submit the form below.
Industry
AEC
Time
100 hours
Location
Singapore
Size
376 ground
floor void
decks
Scanned
Apartment
blocks
Singapore is home to some of the most profitable financial services, manufacturing and oil-refining corporations in the world. But with its accomplishments come some very specific challenges for a city-state which is limited by space but still demanding growth.
Determined to future-proof Singapore’s success, the country has embarked on one of the most ambitious digital twinning projects the world has ever seen – creating a dynamic 3D city model and collaborative data platform, including 3D maps of the region.
At the initial stage of the project it quickly became apparent that aerial imagery alone would not be able to capture ‘void decks’ – open spaces typically found on the ground floor of the city’s apartment blocks. These areas, which are sometimes underneath the tower block structure, are used for everything from games areas, bicycle parking, hosting wedding receptions and wakes, and, as estates grow, facilities such as shops, medical centres and even schools.
In the face of fierce competition, GeoSLAM’s ZEB Revo was selected as the most innovative and efficient solution to collect data from these important community spaces.
“A huge time saving exercise which would ordinarily have taken 40 times longer using traditional surveying methods“
With a handheld “go-anywhere” ZEB Revo, field teams quickly captured a dense and accurate point cloud of an entire void deck, which was then used to model the deck geometry and incorporate this into the existing building models.
376 buildings with void decks were scanned using the ZEB Revo, taking approximately 100 hours – an enormous time-saving exercise which would ordinarily have taken up to 40 times longer using traditional surveying methods.
The ZEB Revo is often used alongside terrestrial hardware as the products are highly complementary. The data output can be easily combined through geo-referencing or scan-to-scan matching and then used to build complete 3D models. In this instance, the combination delivered highly detailed and rapid results while significantly minimising costs too.
Beck Engineering uses GeoSLAM for creating accurate underground maps for mining
Industry
Mining
Scan time
10-15 minutes
Location
Perth,
Australia
Beck Engineering | Words by Evan Jones
“Beck Engineering is an Australian mining engineering consultancy specialising in mining and rock mechanics analysis. Creating highly-accurate underground maps for the mining and natural resource sector is one of the most demanding forms of surveying. Most mine environments are hazardous, and we need to work in tight, enclosed spaces, which are uneven and difficult to access. GPS coverage is, of course, non-existent.
We map mines under intense time constraints using versatile technology which must be adaptable to this tough environment. For this purpose, the we have chosen GeoSLAM’s handheld mobile mapping devices that are compact, portable and deliver a high level of accuracy. With GeoSLAM’s ’go-anywhere’ 3D technology, Beck Engineering has immediate access to invaluable data regarding underground conditions. This time-sensitive information means we can accurately measure the shape of an excavation or tunnel over time.
As a result, tunnels can be faster and better constructed, while being safer and considerably more cost efficient. The applications of the spatially continuous monitoring data collected by GeoSLAM’s devices are being applied to a wide range of geomechanical applications, providing their clients with a previously unattainable insight into rock mass behaviour.
“Laser scanning in underground mines is now routinely conducted to measure deformation, assess residual ground support capacity, determine rehabilitation requirements and confirm excavation stability “
We have continued to use GeoSLAM products as they have proven to be affordable, lightweight and sufficiently robust devices for their application underground. GeoSLAM continue to produce a high-quality device that is at the forefront of practical mobile laser scanning devices.
Industry
Conservation
Scan time
10-15 minutes
Location
Canberra,
Australia
Size
180 square
metre plot
Scanned
National Park
Australian National University
Placed first in Australia and 20th in the world, Australian National University (ANU) is a research institution with its main campus in Canberra, the country’s capital. The university’s research priorities typically reflect the challenges facing the world today. One such project is to track tree growth and development over time in a joint effort between ANU and Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO).
Known as the ‘Precision Measurement of Trees and Forests’ project, the field team is charged with comparing and contrasting different ways of collecting data, using different terrestrial and airborne laser scanners, and working with digital imagery. The survey takes places in the National Arboretum in Canberra which features some 44,000 rare, endangered and symbolic trees and is made up of 94 mini forests.
GeoSLAM’s “go-anywhere” mapping technology was a natural choice for the outdoor project. Unlike terrestrial systems, the splash-proof, dust-tight, mobile laser scanners are designed to operate in the most difficult-to-access spaces, inside or outside, in daylight and darkness – without the need for GPS. What’s more, you can easily attach the portable laser scanners to a drone or helicopter for fast outdoor surveying.
In addition to GeoSLAM’s versatile handheld technology, the team also uses fixed point scanning and traditional forestry measures – such as Suunto and digital photographs from UAV’s. Tom Jovanovic, former CSIRO researcher and now Interactive Technology Specialist at the University of Newcastle, Australia, explains that the technologies are complementary, “Using GeoSLAM from the outset, as well as a different system, has enabled us to compare and contrast different measurements and combine them into a heavily monitored site finding. This includes the high level of resolution being sought.”
“What I really like about this product is that wherever you can walk, you can scan. It really is a case of ‘go-anywhere’ “
Emphasising that the project is specifically designed to take advantage of both static and mobile approaches, Tom Jovanovic says, “What’s nice about scanning with GeoSLAM’s technology is that it doesn’t involve repeatedly setting up in different locations within the research plot. You just initiate the start-up procedure then walk around the plot covering the trees from different angles.”
All forestry professionals like Tom need access to user-friendly technology that is easy to operate but is robust and reliable enough to do the job quickly and accurately. With GeoSLAM scanning technology, he says it takes only 10 to 15 minutes to completely cover a 180 square meter plot, adding, “What I really like about this product is that wherever you can walk, you can scan. It really is a case of ‘go-anywhere’. The scanner has made a significant contribution to an important undertaking. Mobile scanning that gives us dynamic changes over time – from any angle and in 3D – is a very important contributor to this work.”
“Mobile scanning that gives us dynamic changes over time – from any angle and in 3D – is a very important contributor to this work “
These GeoSLAM-delivered findings are vital to the project’s long-term aim. Combining them with knowledge of water usage and photo synthesis, plus meteorological data and high resolution photography, they feed into very fine scale modelling that will guide forestry research management and habitat protection policies into the future.
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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.
