WiMi Hologram Cloud Builds A Holographic Sights-Based HV-SLAM Passive Navigation System
WiMi Hologram Cloud (NASDAQ: WIMI) announced the development of an HV-SLAM passive navigation system based on holographic sights on March 24, 2023. This advanced system enables intelligent devices, like drones, to navigate autonomously using a 3D holographic map. Key components include a depth camera with a laser projector and infrared camera that help the system interpret its environment. Future enhancements will focus on multi-sensor fusion and improved repositioning accuracy. The company emphasizes ongoing optimization and algorithm development to keep pace with industry needs.
- Development of a new HV-SLAM passive navigation system enhances WiMi's technology portfolio.
- The system enables autonomous navigation for devices like drones, tapping into the growing AR market.
- Ongoing algorithm optimization indicates potential initial limitations in system performance.
WiMi'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.
WiMi's HV-SLAM acquires its images through a depth camera. The depth camera contains three core components: a laser projector, a
Feature extraction: obtaining information such as the surrounding environment with the Sensor and recording the feature data.
Map 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.
Dynamic calibration and adjustment: during the movement, new feature landmarks are continuously acquired, and the 3D holographic map model in the system is corrected.
Trajectory annotation: determining position based on the feature landmarks acquired during the previous movement period.
Loop closure detection: checking if the loops can be matched and returned safely.
HV-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 detecting, describing, and matching salient features, image recognition, retrieval, etc., enabling significant efficiency improvements. WiMi's HV-SLAM system helps machines understand the world in front of them geometrically, i.e., by constructing associations in the local coordinate system. The deep learning system enables the device to perform classification inference, i.e., to build associations between different object instances.
WiMi's HV-SLAM also has some algorithms that need to be considered and continuously optimized and upgraded. In the future, multi-sensor fusion, optimized data association, loopback detection, integration with heterogeneous front-end processors, improved robustness, and repositioning accuracy are all the next steps in developing SLAM technology. However, these will be gradually addressed with consumer stimulation and the development of the industry chain.
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