LiDAR and the Smart City

On 19th October 2016, the European Space Agency (ESA) Schiaparelli module was supposed to softly touch down on the surface of Mars to test technology for future landings. Unfortunately this was not to be and communication with the module was lost during entry. Images taken in the weeks following by NASA’s Mars Reconnaissance Orbiter appeared to show that the module had crashed into the surface at high speed. So what went wrong and what has this got to do with LiDAR I hear you ask?

I’ll come back to that shortly…

As a society, right now, we are truly obsessed by data. We use it for everything from generating insurance quotes to predicting the weather to predicting early failure of critical part on aircraft. It is has even been famously used in sports to decide which are the best players for a team to buy. (Ref. the 2003 Michael Lewis book “Moneyball:The Art of Winning an Unfair Game”)!

We also have the concept of Big Data where we collect huge amounts of information, sometimes not even knowing what it may be used for, but just believe that it could be valuable at some future point.

But here’s the problem – what if you can’t rely on the accuracy of the data? The often quoted saying “Garbage in – Garbage out” has never been more true. And if we can’t rely on the accuracy of the data, then we are opening ourselves up to making decisions that are at best suspect and at worst just downright wrong! Therefore, it is vitally important that we not only make the data as accurate as possible, but also that we truly understand what that accuracy is and under what circumstances. The well documented issues with the O-ring seals in the Challenger disaster come to mind here.

For a Smart City to function efficiently it needs good, accurate sources of data in order for meaningful decisions to be made. Be it temperature monitoring, humidity sensors, emissions, cameras or road loops. And let’s be clear here, LiDAR can certainly fit into this bracket. But here’s the rub – data from a LiDAR is only as good as the Perception software that it is connected to, and not all Perception software was created equal. Hence we must understand the strengths and weaknesses of any given Perception before we rely on it, indeed as we should do for any data source.

So what happened to the Schiaparelli module? Well having read this far, it won’t surprise you that it was bad data and its interpretation that caused the crash. After the parachute deployed correctly, it is believed the module unexpectedly began to swing like a pendulum. The Inertial Measurement Unit (IMU) sensed much larger values than expected and this led to the data becoming saturated. The guidance software incorrectly interpreted this causing an altitude calculation that indicated that the module was actually several meters below the surface. The descent thrusters designed to slow entry were therefore turned off and….. well….Boom.