Slam is the abbreviation of simulated localization and mapping, which is used for real-time positioning and map construction, and is often used in unmanned driving, drone navigation and other fields.

SLAM technology can build a map with more realistic visual effects, thus rendering the superposition effect of virtual objects from the current perspective, which is more realistic and without contradiction.

Among VR/AR representative products, Microsoft Hololens, Google ProjectTango and MagicLeap all use SLAM as a visual enhancement method. With the development of artificial intelligence and the wide application of mobile robots in all walks of life, the market demand for SLAM technology is increasingly strong.

Comprehensive SLAM: semantic SLAM, accurate perception, and adaptation to the environment. The effective integration of semantic analysis and SLAM enhances the understanding of interaction in the environment, and endows the robot with complex environmental awareness and dynamic scene adaptation ability.

Broadslam: 654.38+00,000 square meters of powerful surveying and mapping capabilities With efficient environmental identification and intelligent analysis technology, the robot will have 654.38+00,000 square meters of indoor and outdoor full scene mapping capabilities.

Precision SLAM: SLAM2.0, a leading algorithm for high-precision positioning, can identify the boot at any place and locate it globally with an accuracy of 2 cm.

Time-effective SLAM: Real-time update of dynamic maps. According to the data returned by the sensor, it is analyzed and compared with the original map to complete the dynamic real-time update and realize the lifelong SLAM.