Camera that robot carries can provide continuous environment image so that we can extract recognizable and unique information from fixed objects such as walls and shelves, artificial labels such as QR codes, and geometries such as lines and cross points. Such information become unique tags of specific objects in external environment, just like the road signs and doorplates in the city of the real world, enabling robots to judge their own location.
After matching the off-line map with onsite installation layout and marking specific points, robots can calculate the optimal path to go to the destination in a map based on current running condition of multiple vehicles controlled by the system.
Robots extract highly recognizable information from real-time images via vehicle-mounted camera, and match these information with the off-line map, so as to know their location in the map and which direction to go in the next step.
Visual information is the most extensive among all sensor information. We can extract aplenty information from the environment to build the environment map with multiple dimensions and modals, so as to realize positioning and navigation algorithm with stronger stability and better adaptability.
Diversified navigation solution for different scenarios； The cost of visual sensors are much lower than the same level laser sensors； Construction-free, or just some QR codes and slight adjustments； Quick implementation, low maintenance cost； High modularity, easy to retrofit from existing vehicles