This is the final installment of a 5 part series on the essential history, products, and competitive trends that are driving the explosive growth in the commercial drone industry. Its content recently won the prestigious Quora knowledge prize and has contributed to articles from Forbes to CNBC.

Lily exploded onto the web last May with their ridiculously viral product announcement video and a hugely successful preorder campaign. These campaigns generated $34M in presales and a $15M Series A round led by Spark Capital. To make their $999 price point and deliver on the wide array of features shown in the promo video, they’ll need every penny.

To understand my reservations and those expressed by ex-DJI Director of Aerial Imaging, Eric Cheng, who said this in his Phantom 4 breakdown, “If you want a drone that follows you as you run through a forest or while you ski down a hill with trees on it, you should NOT buy any drone. You should get someone to show you what drones can actually do. And you happened to kickstart or pre-order a follow-me drone, try to get your money back ASAP.” Diving in further, let’s analyze the state of the art for intelligent autonomous systems.

A flying robot that follows you around requires a list of increasingly complex things. First, it needs to be able to maintain a stable hover in one place. Companies have figured this out by using GPS and downward facing optical flow cameras that watch how the robot moves with respect to the ground to catch drifting. Now that the robot can reliably stay in one place, it needs to move, just not into the nearby tree. So the robot needs information on its environment which it can get from cameras, sonar, or LiDAR. In each case, the incoming data needs to be heavily processed to be useful so most systems employ what know as SLAM (Simultaneous Localization And Mapping). SLAM first uses the data to build a 3D model/map of the robot’s environment and then figures out where the robot is on that map. This process involves taking thousands of sensor measurements to create a cloud of 3D data points that make the map. Now our robot needs to figure out where its target is and plan how to proceed. After calculating hundreds of options, it selects the optimal path. Only then can the autopilot finally calculate how fast each motor should spin to move the robot in the desired direction.

RECAP: Be stable → build a local map → find yourself on that map → decide where to go → plan steps to get there → execute the first step. Now do this 1000 times per second.

Now we see why some AscTecs come with an i7 processor and 4GB of RAM yet can only autonomously move at a modest walking pace. The time required to figure out the perfect move only allows for a limited amount of moves per second. Researchers have worked around this by collecting less data, planning fewer paths, and accepting greater overall error. This allows the robot to move quicker, but it’s no longer making perfect choices and risks a crash. Allowing robots to autonomously understand and navigate their environment is the single most sought after problem in modern robotics.

Sidebar: You may have seen videos of autonomous drones moving very quickly and completing incredibly precise tasks. These systems cheat by using an array of infrared cameras that track markers on the robot. This is called a motion capture or MoCap system and it can take care of the SLAM processing on a sufficiently powerful and connected desktop computer as long as the robot is in of at least 4 extensively calibrated cameras. If any of the cameras is bumped or moved it can take up to 20mins to recalibrate the MoCap system.

Lily's latest field test footage largely confirms many of its expected shortcomings. The lack of vibration dampening and 3-axis gimbal leave the video quality too shaky and noisy for effective digital stabilization. The motion of drone has to be rather simple to avoid obstacles while tracking the subject and this leaves the camera motion underwhelming compared to the launch video. Finally, the subject is often too far away from the drone and as a result, they seem lost in the scene. All of these shortcomings are due to one critical tradeoff: The Lily camera can be stable, robust, and affordable OR capture interesting and aesthetic video.

What are the best autonomous drones and what can they do? Besides the mysterious work of Skydio, the most advanced demonstrations go to DARPA, MIT, Intel, and Qualcomm. Lily's problem is they are trying to solve one of the hardest aspects of autonomous robotics to create an affordable "follow-me" camera. Don't get me wrong, they skillfully captured the collective imagination of millions, but to bring it to market they will be competing with Yuneec’s manufacturing prowess, Intel’s processors and cameras, and AscTec’s years of autonomous UAS R&D. Even with their viral publicity, huge presales, and strong venture backing the value created by a follow-me camera (for Lily or GoPro) cannot offset the monstrous R&D costs.

However, I believe the rise of the Lily camera is actually a symptom of a deep-rooted problem in the industry and the drone community needs to have a serious conversation about ROI.

Who is making money with drones right now?

Remember in Part 2 when we discussed how photo/video was the best use case for drones? That’s because aerial photo/video holds the best ROI. This is the same reason drones have yet to revolutionize agriculture. As it turns out, the only reliably profitable drone business possible today are:

Photo/Video Hardware and Software High-End Photo/Video Services (this includes all "inspection" services) 3D Modeling Services for Construction, Mining, and Surveying

Everything else is just a proof of concept or prototype. If we go back in time DroneDeploy was a swarm management company and then a cloud-based agricultural data processing tool. Skycatch was building persistent gliders and autonomous data gathering drones that operate out of self-sustaining hangars. Today both companies primarily offer drone data software for 3D modeling.

In their UAS economic impact report, AUVSI predicts 100,000 drones will be sold for precision agriculture by the end of 2016, which is roughly 1 drone for every 10 US farms with annual sales >$10,000. For all of these farms to benefit from UAS, every dollar they spend on buying and operating the drone must create more than one dollar in additional crop sales. For now, manned aircraft and satellite imaging remain cost competitive.

Another factor that heavily influences the ROI of many drone technologies is the FAA’s ban on beyond line of sight operations. This is also why we are unlikely to see drone logistics and package delivery until the 2020s.

And that concludes this series! I hope you found the content enjoyable and I guarantee you now have a strong understanding of the commercial and consumer drone marketplaces, the competitors, the technology, and the underlining economics to create a profitable drone business. Best of luck and happy flying!

Other posts in this series:

Part 1: The Expert’s Guide to Drones: What You Need To Know for 2016

Part 2: Making the $8B King of Drones

Part 3: Is DJI and its Phantom 4 Unstoppable?

Part 4: Yuneec: A Dark Horse The Drone Industry Cannot Ignore

ABOUT THE AUTHOR:

Andy Putch is the co-founder of FreeSkies, a Bay Area software startup that is building the future of the drone UI/UX. Before FreeSkies, he was an autonomous systems researcher with the University of Illinois Bretl Research Group exploring emerging SLAM (LSD-SLAM) and other GPS-denied UAS control systems. He has also worked as an aerospace and defense corporate strategy consultant with Renaissance Strategic Advisors and as a NASA Aeronautics Academy Fellow at the Armstrong Flight Research Center. Andy is a private pilot, Section 333 exempt UAS operator, and featured speaker at several drone conferences and trade shows.