Visualizing Survey Point Clouds on Maps and Sharing via Cloud - LRTK Accelerates On-site DX

Table of Contents

• Introduction: A new era of displaying and sharing point cloud data on maps

• What is point cloud data?

• Methods for acquiring point cloud data

• How to display point clouds on Google Maps and the challenges

• Benefits of sharing point cloud data in the cloud

• Effects of point cloud use on on-site DX

• Simple surveying and point cloud visualization anyone can do with LRTK

• Frequently Asked Questions (FAQ)

Introduction: A new era of displaying and sharing point cloud data on maps

In recent years, the construction and surveying industries have been paying attention to point cloud data (an aggregation of innumerable points obtained by 3D scanning). With severe labor shortages and the need to respond to workstyle reforms (the “2024 problem”), DX-driven efficiency improvements are indispensable, and expectations for point cloud technology as a solution are rising. Point cloud data, which can digitally record an entire site, is expected to be a trump card for on-site DX (digitalization). For example, site conditions that in the past could only be shared via 2D drawings or photos can be understood three-dimensionally with 3D point clouds. If such point clouds are overlaid on a map and shared with stakeholders via the cloud, the site can be made “visible” remotely and used for rapid decision-making.

However, there are many voices such as “I want to display point cloud data on Google Maps but don’t know how” and “It’s hard to share huge point cloud files with the team.” In practice, there are several technical hurdles to handling point cloud data on maps. This article explains the significance and challenges of visualizing survey-acquired point cloud data on maps (especially Google Maps), and cloud-based solutions. Finally, we introduce LRTK, a solution that allows anyone to easily perform high-precision point cloud measurement and sharing. We hope this article helps you advance on-site DX.

What is point cloud data?

First, let’s cover the basics of point cloud data. A point cloud is a collection of innumerable points obtained by laser scanners, LiDAR sensors, photogrammetry, and the like. Each point contains X, Y, and Z coordinate values and can precisely represent the shape of terrain or structures. In other words, point cloud data is 3D data that digitally copies real space with countless measurement points. Points in a point cloud may also have attribute data such as color information or reflectance intensity, enabling more realistic reproduction of site conditions.

Traditionally, acquiring point cloud data required expensive surveying equipment and specialized skills, but methods have diversified with technological advances. In addition to ground-based and drone-mounted laser scanners, it is now possible to easily acquire point clouds using LiDAR built into smartphones and tablets. For example, on the latest smartphones, you can obtain short-range 3D point clouds simply by scanning the surroundings with a dedicated app. As point cloud technology becomes more accessible, the demand to utilize acquired point clouds on site maps is also growing.

Methods for acquiring point cloud data

There are various methods for acquiring point cloud data. Here are representative ones.

• Terrestrial 3D laser scanners: High-precision laser scanners mounted on tripods. They are suitable for detailed measurements of building interiors and structures, and can acquire high-precision point clouds with millimeter-level (0.04 in) accuracy in a short time.

• Vehicle-mounted LiDAR (mobile mapping): LiDAR sensors mounted on vehicles or drones to scan wide areas while moving. Suitable for large-area surveys such as roads, rivers, and forests, enabling efficient acquisition of point cloud data over vast areas in a short time.

• Handheld laser scanners: Handheld or backpack LiDAR devices used while walking around the site. They are effective for measuring narrow indoor spaces and complex structures, and some can generate point clouds in real time.

• Photogrammetry (SfM/MVS): A technique that creates 3D point clouds from image analysis by photographing an object from various angles with a normal digital camera. Because it reconstructs 3D from photos without using lasers, it can be introduced with relatively inexpensive equipment, and terrain point clouds can be created from drone photos.

• Smartphone LiDAR/camera: A recently emerged method that uses LiDAR sensors or high-performance cameras built into smartphones and tablets to obtain point clouds. With a dedicated app, anyone can easily perform short-range 3D scans. Although accuracy and range lag behind professional equipment, they have reached a level sufficient for simple on-site surveying. For example, the latest iPhone and iPad Pro include LiDAR, and the widespread availability of such devices has greatly expanded the adoption of point cloud measurement.

Each method differs in measurable distance, accuracy, and necessary equipment, so it is important to choose appropriately according to site conditions and purpose.

How to display point clouds on Google Maps and the challenges

If 3D point cloud data could be displayed on Google Maps, anyone could intuitively check site conditions on a familiar map screen. However, currently, common map services like Google Maps do not provide functionality to directly display raw point cloud data (some convert point clouds to KML and display them in Google Earth, but this is impractical for large datasets). Therefore, overlaying point clouds on maps requires the following kinds of measures.

• Transformation to surveying coordinates: Point cloud data must be aligned to map coordinate systems (latitude/longitude or planar coordinates). The coordinate systems of point clouds obtained by laser scanners or smartphone LiDAR are local to each device and do not match map positions as-is. Traditionally, it was essential to georeference point clouds afterward based on surveyed control points on site.

• Data format conversion: To display point clouds on Google Maps or Google Earth, you need to convert them to compatible formats. For example, some approaches convert point clouds into simplified 3D models or KML files to plot them in Google Earth. However, this method often limits the number of points and diminishes depth and detail.

• Use of dedicated viewers: To combine point clouds with web maps, you can use specialized web point cloud viewers or GIS software. These allow overlaying point clouds on maps (aerial photos or plans) as a background, but they require expertise and server environments, making them difficult for ordinary users to handle easily.

• Display performance issues: Rendering a large number of points in a browser requires high processing power. Even if you can plot point clouds directly on a map, datasets with millions of points will slow performance and make smooth interaction difficult. To achieve comfortable real-time display, downsampling and rendering engine optimization are indispensable.

As described above, displaying point clouds on Google Maps requires coordinate and data transformations, and use of specialized tools, involving multiple steps and knowledge. As a result, companies often fail to fully share and utilize acquired point cloud data internally. Recently, attempts to visualize site conditions by linking point cloud data with Google Street View have emerged, but specialized systems are still required.

Benefits of sharing point cloud data in the cloud

Sharing huge point cloud datasets among multiple stakeholders is effective when done via the cloud. Traditionally, sharing point cloud files (hundreds of MB to several GB) required mailing hard drives or carrying USB sticks. If point clouds are uploaded to the cloud, authorized personnel can download and view them anytime via the internet.

The main benefits of sharing point cloud data in the cloud include:

• Simplified data distribution: Even very large files can be shared with one click via the cloud. Files too large for email attachments can be shared with a single URL, smoothing data exchange between site, office, and subcontractors.

• Always sharing the latest data: By referencing cloud-hosted point clouds, everyone can view the latest version. When someone edits or adds scan data, updating it in the cloud reflects changes immediately, preventing mistakes based on outdated files.

• No need for high-performance PCs: Because the cloud service handles rendering and analysis of point clouds, users can view point clouds in a browser regardless of their local PC specs. There is no need for everyone to install dedicated software, and site 3D data can be checked from tablets.

• Efficient data management: If you organize large amounts of point cloud data in the cloud by project, you can select and display only the necessary area and easily compare with past data. Cloud services with permission management allow sharing only required parts with external vendors.

• No concern about differing formats: Even if recipients don’t have dedicated software, a unified cloud viewer lets them share 3D data without worrying about file format compatibility, enabling smooth utilization across departments and companies.

• Data preservation and backup: Storing data in the cloud reduces the risk of data loss due to PC failure or loss. Many services offer automatic backups and version history, allowing safe accumulation of point cloud data.

By leveraging the cloud, sharing and using point cloud data becomes dramatically more efficient. Remote team members can discuss while viewing the same 3D data, enabling situation assessment and instruction without visiting the site. This leads to DX effects such as faster decision-making and reduction of duplicated work.

Effects of point cloud use on on-site DX

Utilizing and sharing point cloud data on maps via the cloud can significantly transform on-site workflows. First, shortening the lead time from measurement to sharing greatly speeds up decision-making. For instance, processes that used to take several days to convert survey results into drawings and report them can, with point clouds, be shared via the cloud on the same day, allowing meetings and design reviews to begin immediately. In some sites, the introduction of point cloud measurement reduced as-built measurement time that previously took more than half a day to about 30 minutes.

Also, DX benefits include progress toward paperless and remote collaboration. Since point clouds can be viewed in the office without visiting the site, travel time is reduced and safety is improved (avoiding dangerous site entry). The Ministry of Land, Infrastructure, Transport and Tourism’s i-Construction initiative also encourages use of 3D data for as-built management. The ministry aims to improve construction productivity by 20% by 2025, and the digital twinning of sites using point cloud data is a pillar of DX. Recording completed infrastructure as point clouds also helps future maintenance planning and rapid situational assessment during disasters.

Furthermore, point cloud utilization contributes to on-site labor saving and workforce reduction. A single scan can capture countless points, allowing one person to complete surveying tasks that previously required multiple people and much time. In industries facing severe labor shortages, improving work efficiency through digital technology is indispensable. Point cloud utilization is a powerful tool that concretely advances on-site DX. Additionally, it is easy to extract arbitrary cross-sections from acquired point clouds or reuse them to create design drawings, adding further value. Including these advantages, point cloud usage becomes a core technology of on-site DX.

Simple surveying and point cloud visualization anyone can do with LRTK

Finally, we introduce the latest solution LRTK, which dramatically simplifies point cloud acquisition and sharing. LRTK is a high-precision GNSS positioning system using a smartphone, a “pocket-sized surveying instrument” that anyone can operate. By attaching the LRTK device to a smartphone and using a dedicated app, you can obtain high-precision coordinates simply by walking on site. By linking with the smartphone’s built-in LiDAR sensor, you can scan the surroundings and generate 3D point cloud data in real time with accurate coordinates assigned to each point.

Point cloud data acquired with LRTK are coordinate-attached point clouds already aligned to surveying coordinates (public coordinate systems) from the start. Without cumbersome post-processing for georeferencing, you can display point clouds in the correct positions on a map immediately after acquisition. These coordinate-attached point clouds can be overlaid with CAD drawings and other geospatial information instantly. LRTK also integrates with cloud services, allowing scanned data to be automatically uploaded to the cloud on site. You can visualize point clouds obtained on site instantly in a cloud map viewer and share and review them with your office team. LRTK supports augmentation signals provided by Japan’s quasi-zenith satellite system, so high-precision positioning can be maintained even in mountainous areas outside network coverage.

Another feature of LRTK is intuitive operation that requires no specialized knowledge. Actions such as starting positioning or scanning are performed with a single tap, and then you simply walk with the device to acquire precise point clouds. For example, beginners can complete detailed point cloud scans of a large site in just a few minutes and immediately put the data to use. Point cloud measurement and utilization, which once required expensive equipment and skilled operators, become suddenly accessible with LRTK. As you promote on-site DX, LRTK — enabling “anyone to easily perform 3D surveying” — will be a powerful partner.

Frequently Asked Questions (FAQ)

Q: Can I display point cloud data on Google Maps? A: At present, Google Maps alone does not support direct display of point cloud data. You need a dedicated cloud service or 3D viewer. For example, LRTK can display acquired coordinate-attached point clouds on the cloud with a map background, allowing point cloud viewing via a browser in a way similar to Google Maps.

Q: Can it be used without specialized knowledge? A: Yes. LRTK is designed with simple operability in mind and is intuitive even for first-time users. By following app instructions and pressing a button, positioning and scanning start, and high-precision point cloud data are acquired automatically. No difficult settings or manual operations are required, so people without surveying expertise can use it safely. Manuals and support are also provided at introduction, so you can quickly resolve any questions.

Q: What equipment is needed for point cloud measurement? A: Traditionally, fixed 3D laser scanners, drones, and surveying GNSS equipment were required, but with LRTK, large-scale equipment is unnecessary. A LiDAR-equipped smartphone (e.g., iPhone Pro models), the LRTK compact GNSS receiver device, and the dedicated app are enough for high-precision point cloud measurement and cloud sharing. Your smartphone becomes a high-precision surveying instrument, reducing initial investment. For stable high-precision measurement, a pole or monopod to fix the smartphone is ideal, but for simple use cases, handheld operation is sufficient.

Q: Is the accuracy good enough when measuring with a smartphone? A: LRTK dramatically improves smartphone positioning accuracy via satellite positioning (RTK). Horizontal errors are on the order of several centimeters (a few inches), comparable to traditional specialized equipment. Combined with a smartphone’s built-in LiDAR point cloud, you can obtain high-precision 3D point clouds with almost no positional offset. This accuracy is sufficient for professional applications such as civil surveying and as-built management. Vertical accuracy is also within about 2–3 cm (0.8–1.2 in). Note, however, that positioning is not possible in underground spaces or tunnels where GNSS satellite signals cannot reach.

Q: In what sites and applications can it be used? A: It can be used in a wide range of fields, including civil surveying and as-built management, setting out for foundation work, calculating earthwork volumes for embankments and excavations, damage recording in disasters, maintenance management of infrastructure such as roads and bridges, and forest monitoring. In short, point cloud data is highly useful whenever you need to accurately record and share on-site shapes and dimensions digitally.

On-site DX cannot be achieved overnight, but steady progress is possible by adopting new technologies. Visualizing survey point clouds on maps and sharing them in the cloud is a powerful means to that end. Please consider using LRTK to take your company’s on-site DX to the next stage.