The most accurate wearable mobile mapping device on the market

The absolute accuracy of a mobile mapper is very complicated to define with a single number. This is because they process the final point cloud using SLAM technology – and SLAM performance varies depending on numerous real-world factors, such as the geometry of the environment you’re scanning. (This is why you often see absolute accuracy numbers listed on spec sheets as a range – for instance, 6-15 mm.)

Before a vendor can make generalized statements about the accuracy of any given mobile mapper, they will have to perform extensive testing, in a variety of scenarios, and see how the system works in each of the real-world scenarios where it would be used.

→ To provide some meaningful answers about mobile scanning accuracy, NavVis developed a rigorous methodology for validating accuracy, open to independent review and scrutiny.

If you lined up three systems with identical form factors and sensor payloads, you could still expect the systems to return results that vary significantly in quality. Why is this?

It comes down to the quality of the SLAM implementation. Here are two of the primary factors that set one SLAM system apart from another.

ROBUSTNESS

In the real world, SLAM systems will find some environments more challenging than others. Here’s a good example: A long hallway with minimal doorways lacks the features that the SLAM needs to track your position. This can cause errors in the trajectory data generated by the SLAM and degrade the accuracy of the final point cloud.

More robust SLAM can handle more of these types of situations and handle them better. They produce a better trajectory, and, in turn, more accurate final point clouds.

ERROR CORRECTION

The environment isn’t the only thing that can create errors in a mobile data set. Errors also come from the sensors themselves—all sensors produce a certain amount of noise, which can add up to tiny deviations in the SLAM estimate. Over time, that accumulates to a problem called drift.

That’s why virtually every scanner on the market offers features that correct errors and improve the accuracy of your final data set.

However, not all SLAM systems are created equal in this regard. Most of them offer loop closure, which corrects for errors when you return to a spot you’ve already scanned. But not all loop closure technology will produce the same results. And some systems offer control-point functionality for locking the trajectory data to surveyed control points, but most do not.

In short, some SLAM systems have been designed to handle the complexities of real-world scanning better than others. This difference shows clearly in the results.

It comes down to computing power.

As discussed above, a SLAM mapping system fuses data from a variety of sensors to produce a point cloud. The list includes IMUs that track the device’s orientation, HD cameras that snap large, colorized images, and multiple lidar units that record 300,000 points (or more) a second.

The challenge here is that the sensor payload produces a huge amount of data - too much for the computer in a mobile device to process easily.

As a result, each manufacturer needs to pick its priorities for data processing. Some design their devices to generate point clouds in real time and compromise on quality. Others choose to process the data more slowly, but produce higher quality results. Another group gives you the option to select real-time processing or higher-quality processing, depending on the needs of your project.

NavVis has chosen yet another approach. The company’s mappers process data while you scan to display real-time visual feedback on your tablet. Then, they use more powerful computers back in the office to finalize the data and produce point clouds of the highest quality.

Because a mobile scanner captures continuously as you walk.

Where a terrestrial scanner captures each measurement point once during a scan, a SLAM system automatically captures each measurement numerous times, from multiple angles, as you move through the asset.

This gives the post-processing software a large set of possible x, y, and z values for each point. It performs complex analysis on these values, enabling it reduce or even eliminate uncertainty that arises from physical phenomena like sensor noise.

The result? It can produce a point cloud that is more accurate than the sensor is specified for.

A SLAM device is a lot more than its hardware—it relies heavily on software to produce a final point cloud. By updating and tweaking that software, a manufacturer can upgrade your device long after you’ve made the initial purchase.

Some vendors continue to develop their software to make improvements to SLAM processing, live visualization, and the quality of their post-processing. They release these improvements as software updates, so you can simply download and install them on your device or your computer to upgrade. Voila: you now have the latest technology.

→ The software in mobile mapping systems offer one very important benefit; owners can expect their tools to continue evolving long after the initial purchase.