Robots, Autonomous Driving Vehicles, and Maps
There is a kind of data that you must have for robots to be able to move about in our everyday spaces and for autonomous driving vehicles to be able to move about safely on our roadways. It is “maps.” Actually, these maps we’re talking about are a bit different than the map apps or navigation systems we are familiar with. They are machine readable, 3D/HD maps.
These maps play an extremely important role for robots or autonomous driving vehicles. This is because robots or autonomous driving vehicles rely on these maps for location recognition and route planning, things that humans can do naturally. This is why HD maps are called a part of the brain of autonomous driving vehicles.
Therefore, NAVER LABS is continuously developing a solution for creating 3D/HD maps. We are creating HD maps both indoors, using the mapping robot M1, and on the roadways, through the mobile mapping system R1 and aerial maps.
However, there is still one more problem that needs to be solved. Updates.
The form of the world is always changing. Therefore, for maps, keeping them up-to-date is akin to accuracy. Maps for robots or autonomous driving vehicles are no different. At NAVER LABS, as well, techniques to help with this problem are being researched, utilizing robots, AI, MMS (mobile mapping system), etc.
Technology where Robots and AI Find Changed Shop Names
Last year, we developed “self-updating map” technology that automatically discovers changes in shops within large-scale indoor spaces. Robots move about expansive and complex commercial spaces and accurately pick out changed shop names. To automatically analyze the images collected by the robots, computer vision and deep learning technology was also utilized.
But since shopping malls are filled with so much visual information, it was very important to be able to differentiate from advertisements, people walking about, etc. and accurately perceive information about the shops. The algorithm developed at NAVER LABS to achieve this can very accurately perceive when a shop has newly opened, closed, or changed, or when just the name of a shop has changed, and the results of this have been presented at a computer vision/pattern recognition (CVPR) conference.
Technology that Automatically Updates HD Road Maps
This year, we are progressing with the ACROSS project to expand this type of updating technology to our roadways. Of course, the environment and conditions are very different from those indoors.
ACROSS utilizes a method where mapping devices made up of low-cost sensors are installed on multiples vehicles which then all simultaneously identify changes in roadway information.
The image data collected by the mapping devices is likewise analyzed by AI. It detects changes in the existing HD maps’ road layout (lane information, stop line locations, road markers, etc.) or 3D information (traffic signs, buildings, traffic lights, street lights, etc.). In reality, it must also sense changes in the seasons, time, and weather as well, and also be able to distinguish well between the cars on the roads. It is a task that is challenging in many ways, but we are continuously figuring things out.
In the future, robots and autonomous driving technology will slowly break free from the lab and permeate our lives. To accomplish such an end, two things we have to prepare are HD map creation technology and an updating solution.
To be more accurate, and always up-to-date! We are researching technology to accomplish this end.