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WiMi Hologram Cloud Builds A Holographic Sights-Based HV-SLAM Passive Navigation System
BEIJING, March 24, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR")
About this update from Wimi Hologram Cloud Inc.
[{"type":"text","content":"BEIJING, March 24, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) (\"WiMi\" or the \"Company\"), a leading global Hologram Augmented Reality (\"AR\") Technology provider with years of investment in holographic sights 3D map construction in the field of SLAM, today announced the development of an HV-SLAM passive navigation system based on holographic sights.\nWiMi's HV-SLAM is passive navigation, a technological application of self-cruising positioning. HV-SLAM is crucial to the mobility and interaction capabilities of intelligent devices such as drones, as it represents the basis for such powers: knowing where you are, knowing what your surroundings are like, and thus knowing what to do next autonomously. It can be argued that any intelligent body with mobility has some form of SLAM system.\nWiMi's HV-SLAM acquires its images through a depth camera. The depth camera contains three core components: a laser projector, a DOE, and an infrared camera. Their role is to help the system form a 3D holographic map so that the device can better determine its course of action and how to move intelligently. As an example, when people come to an unfamiliar environment and want to familiarize themselves with the environment and complete tasks quickly, then the system will need to do the following:\nFeature extraction: obtaining information such as the surrounding environment with the Sensor and recording the feature data.\nMap construction: Based on the information acquired by the Sensor, the environmental features are constructed in the system in the form of a 3D holographic map.\nDynamic calibration and adjustment: during the movement, new feature landmarks are continuously acquired, and the 3D holographic map model in the system is corrected.\nTrajectory annotation: determining position based on the feature landmarks acquired during the previous movement period.\nLoop closure detection: checking if the loops can be matched and returned safely.\nHV-SLAM's algorithm builds a real-time 3D holographic world map and tracks the camera's position and orientation. By combining CNNs with deep learning, the system can self-correct passive position navigation. HV-SLAM focuses on geometry and space, while deep learning is perceiving and recognizing objects to give advice. Computer vision technologies applied to visual SLAM include detecti...