What is SLAM technology for sweeping robots?

Just like submarines, robots cannot always rely on GPS, especially sweeping robots used in indoor scenarios. GPS has an accuracy of a few meters outdoors. Relying on GPS cannot allow sweeping robots to safely avoid the legs of the coffee table and clean the sofa. bottom. Relying on SLAM technology, they can observe and map the surrounding environment on their own, build a navigation map through sensor data calibration, and then understand where they are, where they should go to sweep the floor or go back to the corner to charge. We can summarize the simple technical thinking of SLAM: Without prior knowledge, the surrounding environment information can be acquired through sensors, and the environment map can be constructed quickly and real-timely, and at the same time, it can solve its own position and complete subsequent tasks such as path planning on this basis. Does it sound simple? But in fact, SLAM is a complex multi-stage task, including collecting various types of raw data (laser scanning data, visual information data, etc.) in the actual environment through sensors, and calculating the relative position estimation of moving targets at different times through visual odometry (including features) Matching, direct registration, etc.), through the back-end module to optimize the cumulative error caused by the visual odometer (traditional filtering algorithm, map optimization algorithm, etc.), and finally generate a map through the mapping module (of course, it usually needs to be equipped with loop detection for Eliminate accumulated errors in space, etc.) to achieve the purpose of mapping and positioning.