Using lidar-equipped robots, Doxel scans construction sites every day to monitor how things are progressing, tracking what gets installed and whether it’s the right thing at the right time in the right place. You’d think that construction sites would be doing this by themselves anyway, but it turns out that they really don’t, and in a recent pilot study on a medical office building, Doxel says it managed to increase labor productivity on the project by a staggering 38 percent.

The concept behind Doxel is straightforward enough: Construction projects have plans and budgets and timelines. If you stick to the plan, the budget and timeline (the things you really care about) should be what you expect. But construction projects with plans and budgets and timelines all depend on a big pile of humans doing exactly what they’re supposed to do, and we all know how often that happens.

Doxel mostly focuses on what’s going on inside a construction site, since that’s where the majority of the complicated stuff happens. Once the construction site shuts down for the day (usually in the late afternoon), Doxel sends in a cute little autonomous robot with an excellent lidar on it to methodically scan the entire site. The robot has no problem following prescheduled paths that can include stairs, and just one of these little things can scan about 30,000 square meters over the course of a week.

Once the robot is finished, it sends all of that data to the cloud, where it gets chewed on by Doxel’s 3D semantic deep-learning algorithms, which have been trained to recognize all kinds of components (even if only a tiny bit of them is visible) based on shape, location, and size. The accuracy of the lidar map that Doxel creates allows them to verify that the right things have been installed correctly and in exactly the right place. If they have, Doxel can quantify that progress, and if they haven’t, Doxel can send in the killbots. Or, they can just let managers know so that they can take steps to fix things immediately.


Source: IEEE Spectrum