Geospatial mapping has become more important now than ever before; driven by the unstoppable growth of automated decision making. From self-driving vehicles that can navigate a specified route to a drone that can fly over a construction site, autonomous decision making requires the next level of digitalization. Each aspect of the physical world requires layers of digital signatures to be embedded within a virtual representation of the necessary space. The level of information that’s required for the representation is ever increasing and each enable new use cases. Digitization is not just on a growth curve, but on a hockey stick growth curve, moving at a rapid pace and introducing new autonomous use cases.
The future is machine networking
Mapper’s core capabilities enable self-driving vehicles and machines of different autonomous levels to navigate set routes by providing layers of up-to-date information in a machine-readable map. Mapper has created a globally scalable ecosystem for on-demand creation, validation and maintenance of centimeter-accurate maps. Mapper’s cutting edge technology allows customers to request a map based on their unique geographic needs — think entire cities for robotaxi fleets, sections of highways for truck drivers, shipping container lots for autonomous machinery.
As more and more innovative autonomous technology gets developed, automakers will begin collecting high fidelity data themselves. So at that point, the question becomes who is going to be the aggregator of that data, and that is where Mapper will play a key role. What Mapper is trying to enable is a network effect wherein in the future, autonomous mapping data can be automatically converted into meaningful information for various use cases.
Until recent autonomous vehicle developments, geospatial mapping has been focused on consumer use cases, such as Google Maps or Apple Maps. Making maps visually pleasing and readable for people has always been the main priority. However, machines do not need a positive user experience, but rather require high precision and up-to-date accuracy. A machine doesn’t care to explore the world through a map, but needs programmed direction to take it from point A to point B and complete the task at hand. The requirement for no-frills technology to help an autonomous machine navigate lends itself to a new kind of a technology that Mapper is developing. Mapper hopes to grow the company as the industry matures and as automation becomes a part of daily life.
Making mapping relevant
The technology that Mapper is developing takes raw data from the streets and converts it into a map without human intervention – just data in and maps out. That is the core machine learning AI capability that Mapper built in-house. In any given city where no map exists, or a city where a map has been built and hasn’t been updated for a very long time, Mapper can automatically convert data capture to map creation and automate it. What these geospatial machine maps will enable in the future is automation in the form of autonomous driving, driver assistance capability, drone capabilities and beyond, because maps are a fundamental piece of the environment meeting a machine.