These Surveyors Were Made For Walkin’: The Impact of 3D Mobile Mapping
Written by Raffi Jabrayan, Vice President, Business Development & Commercial Sales at Exyn Technologies
In the mining industry, the analysis of underground cavities has traditionally relied on terrestrial laser scanners that are mounted on booms or tripods to capture detailed scans. These devices emit laser beams and measure the time it takes for the beam to return after hitting a surface, thus creating detailed 3D models of the mine structure.
Often positioned at key points within the mine, these scanners provide comprehensive data sets on the integrity of the mine, potential geological hazards, and the extent of mineral deposits. Despite their widespread application, these technologies have inherent limitations, including the inability to reach inaccessible areas and the time-intensive process of manual data collection.
Underground cavities are incredibly complex and unique. Most laser scanners being used today require survey teams to stay stationary throughout the entirety of the scan for the most accuracy. But this fixed location can create gaps in a complete scan, often referred to as ‘shadows’ in the industry. These shadows are a huge risk for mining operations.
At a minimum, these shadows create inconsistencies in 3D point clouds maps providing survey teams with an incomplete picture of ongoing operations. Shadows pose a safety risk as well by not delivering a complete map of an underground cavity – how does the survey team know if it’s safe to approach a new berm? Or the shadow could be obscuring a cavity present out of the surveyor’s field of view. That could lead to an improper calculation of the amount of ore available in a stope. These inconsistencies build over time which can lead to bad assumptions, improper reporting, excess employee time underground, and lost business.
A technological SLAM dunk: simultaneous localization and mapping
However, the high-tech world of robotics and AI has an elegant solution to this survey-shadow situation. Roboticists have been trying to solve a complex problem: how can we teach a robot to intelligently navigate through a new environment it’s never seen before. Ideally, we need the robot to create a map in real time while also localizing itself within that map. This is called Simultaneous Localization and Mapping, or SLAM. And as if that problem wasn’t hard enough, to work in a mining environment we need to find a sensor that can perform with little to no light.
You can power a SLAM algorithm with a variety of sensors, but the most common are either Vision-based or LiDAR-based SLAM. Vision-based SLAM is powered by a traditional, albeit very small, camera sensor that uses features captured in photos to localize itself in the map.
LiDAR (Light Detection and Ranging), on the other hand, is a light-ranging technology that uses lasers to measure distances and generate precise, 3D information about the shape and surface characteristics of the surrounding environment. It’s like having a high-resolution, 3D camera that can see in all directions and capture every detail down to the millimeter. That’s why LiDAR-based SLAM is the most popular of the 3D mobile mapping payloads.
The advent of LiDAR-based 3D mobile mapping payloads has rapidly increased the speed and efficiency of data capture in a variety of industries, including mining and construction. These tools have several advantages over traditional stationary tripod laser scanners, which have been commonly used for surveying tasks.
Mapping on the move with LiDAR-based SLAM
One significant benefit of mobile 3D mapping tools is their mobility. Unlike stationary scanners, which can take 10 to 20 minutes to set up and can only scan from a fixed position, 3D mapping devices are designed to be portable and flexible. Meaning you can map an area on the go and capture data from different perspectives, providing a more comprehensive view of the survey area. These tools can also be mounted on vehicles or drones, enabling them to cover larger areas and reach places that might be difficult or dangerous for humans to access.
Another benefit of a LiDAR-based SLAM 3D mobile mapping tool is that the algorithm can accept a variety of sensor inputs. Meaning we can use additional sensors to overlay even more important information into our 3D data set. You could include an oxygen or carbon dioxide sensor to increase the situational awareness of survey teams. Or you could even include a camera sensor to overlay real-time RGB visual data on the point cloud for an even more realistic digital twin.
3D mapping tools have seen a significant surge in popularity, particularly within survey teams in the mining industry. The ExynPak, for example, has made a name for itself as a reliable, high-accuracy mapping tool that comes in a portable package. This device stands out for its real-time 3D mapping capabilities and survey-grade accuracy, making it a leader in the field. The ExynPak’s rugged design allows it to be mounted on a vehicle while still being portable enough to be carried by hand. Its ability to provide real-time point cloud colorization also sets it apart. As such, tools like the ExynPak are not only transforming how surveying is conducted but also contributing to the safety and efficiency of operations in the mining industry.
However, the biggest sticking point with 3d mobile scanning is also the most important for surveyors: accuracy. SLAM is great with local accuracy, knowing how close or far objects are to each other in its environment, but has struggled with global accuracy, aligning that 3D point cloud to a real-world map. But huge advancements have been made to increase this global accuracy to survey grade levels more than accurate for geospatial mapping teams.
The power of post processed point clouds
The post-processing suite on the ExynPak, for example, has gained a reputation for its robustness and efficiency in handling point cloud data. This proprietary software comes with an advanced algorithm that can quickly smooth and downsample point clouds, significantly improving the digital twin workflow for survey teams. After the survey team captures a scan with the ExynPak, the log can be quickly processed, and a precision point cloud is easily exported for all downstream software.
One of the key features of this post-processing suite is its ability to reduce drift, a common issue in SLAM-based mapping that can lead to inaccuracies over long distances or extended periods of time. And to increase global accuracy, the post-processing supports georeferencing using ground control points, which snaps the map into alignment with previously known points. By providing a more streamlined and reliable data processing solution, ExynAI is helping to drive the transformation of surveying practices in various industries.
It’s clear that LiDAR-based 3D SLAM mapping is revolutionizing the world of surveying and mapping, offering a more efficient and flexible alternative to traditional stationary laser scanners. These mobile mapping systems provide numerous benefits – from their portability and advanced feature set to their high-accuracy mapping capabilities and time efficiency.
Looking to the future, the potential impact of mobile 3D mapping is vast. As the technology continues to evolve we can expect even greater accuracy, faster processing times, and more sophisticated features that will further transform how we capture and interpret spatial data. This has implications for a wide range of applications, from construction and mining to urban planning and environmental research.
If you’re curious about the future of LiDAR-based 3D SLAM mapping and would like to continue the conversation contact Exyn here.
About the Author
Raffi Jabrayan is the VP, Commercial Sales and Business Development for Exyn Technologies. He oversees the expansion of the business internationally in the mining and construction sectors, as well as penetration into other industries.
A large part of his role at Exyn is to help miners leverage the data produced by Exyn’s autonomous aerial robots to streamline underground inspections, enhance operational efficiency, and reduce risk.
Prior to joining Exyn, Raffi managed digital and technology innovation projects for Dundee Precious Metals and was intimately involved with operationalizing new technologies into Dundee’s workflow. Raffi oversaw the scouting, due diligence, implementation, and post integration assessment of Dundee’s digital and technology projects.
Raffi is a seasoned mining professional with practical experience at both the plant and corporate level in various capacities and has completed the Digital Business Strategy Program at MIT Sloan as well as Driving Strategic Impact from Columbia Business School.
Exyn Technologies is pioneering multi-platform robotic autonomy for complex, GPS-denied environments. For the first time, industries like mining, logistics and construction can benefit from a single, integrated solution to capture critical and time-sensitive data in a safer, more affordable and more efficient way. Exyn is powered by a team of experts in autonomous systems, robotics and industrial engineering, and has drawn talent from Penn’s globally recognised GRASP Laboratory as well as other prominent research institutions. The company is VC-backed and privately held, with headquarters in Philadelphia.
