An MIT spinout, Essess, is helping combat energy waste from homes by providing cars with thermal-imaging rooftop rigs that create heat maps of buildings as they drive by. The cars can image thousands of homes and buildings per hour, detecting fixable leaks in windows, doors, walls, and foundations to help owners curb energy loss.
About the size of a large backpack, Essess’ rig includes several longwave infrared radiometric cameras and near infrared cameras. These cameras capture heat signatures, while a lidar system captures 3D images to discern building facades from the physical environment. An onboard control system has software to track the route and manage the cameras.
On the software side, computer vision and machine-learning algorithms stitch together the images, extract features, and filter out background objects. In one night, the cars can generate more than three terabytes of data, which is downloaded to an onboard system and processed at the startup’s Boston headquarters.
Combining those heat maps with novel analytics, Essess shows utilities companies which households leak the most energy and, among those, which owners are most likely to make fixes, so they know where to direct energy-efficiency spending. This may include sending customers the thermal images of their homes along with information on the fixes that could offer the most return on investment.
But the startup also works with the US Department of Defence to help identify energy-wasting buildings on their bases. And schools, municipalities, oil refineries, and other organisations have hired Essess to scan their facilities and find, for instance, fixes that might affect their heating bills in the winter, have a short payback period, or are within a certain budget.
‘We’ve made thermal imaging very automated on a very large scale,’ said Essess co-founder Sanjay Sarma.
Founded in 2011, the startup has since mapped more than four million homes and buildings in cities across the United States for military, commercial, and research purposes.
The company developed an algorithm called Kinetic Super Resolution – co-invented with Sarma and MIT postdoc Jonathan Jesneck – that computationally combines many different images taken with an inexpensive low-resolution infrared camera to produce a high-resolution mosaic image. This allows less expensive infrared cameras to be used.
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