The testing and training in the field that’s required to ensure that new drone AI technology is safe and effective is a major barrier to entry – and a major expense. It’s a problem that big data company CVEDIA is solving with a solution they call SynCity.
AI and drones have been a hot combination in recent months, as the commercial applications for artificial intelligence combined with an aerial platform abound. Verticals including surveying and mapping, agriculture, construction, law enforcement, surveillance and security, weather monitoring, inspections and insurance are all recognizing the benefits of AI to power drone technology. That’s not including other areas like self-driving cars and other autonomous vehicles, also heavily reliant on AI advances.
SynCity is a photorealistic simulator able to create any type of scenario with accuracy to train that intelligence: scenarios like agricultural fields, mining dens, bridges over water. The platform is also able to simulate the sensors used on drones: LiDAR, cameras, IMU, or GPS, for example.
“The benefits of using a simulator versus real-world data or in conjunction with real-world data is a major reduction in both costs and damage to the drones as well as the sensor equipment as a result of crashes,” says Natalia Simanovsky, SynCity’s Business Development Lead.
“The entry point for drones using LiDAR, for example, is a minimum of $50K all the way up to a quarter of a million dollars,” says Simanovsky. “Think of the costs one would save if they could simulate near collisions as opposed to testing the drones externally.”
It’s not only a case of expense, Simanovsky points out, or losing the drone and payload. There are serious inherent dangers and risks in many industrial or military applications. “SynCity can create a simulation system where the drone believes that it is flying outside, capturing those elements of danger.”
Simanovsky says that there has been a long running debate about whether or not AI can be trained on synthetic data. But as far as the SynCity team is concerned, the debate’s been put to rest. “We’ve been able to train AI models successfully using 100% synthetic data,” says Simanovsky. “It’s the approach that you take – the level of photorealism is important. Whatever scenario you are creating, you have to make that environment pretty realistic.”
Major companies like leading thermographic technology provider FLIR and other drone solution providers agree, using the platform to simulate a forest, as demonstrated below, or flight over open water. By cutting the costs and time to train new platforms, SynCity may be the next step driving AI technology forward.
Miriam McNabb is the Editor-in-Chief of DRONELIFE and CEO of JobForDrones, a professional drone services marketplace, and a fascinated observer of the emerging drone industry and the regulatory environment for drones. Miriam has penned over 3,000 articles focused on the commercial drone space and is an international speaker and recognized figure in the industry. Miriam has a degree from the University of Chicago and over 20 years of experience in high tech sales and marketing for new technologies.
For drone industry consulting or writing, Email Miriam.
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