An HV-SLAM passive navigation system based on holographic sights has been developed by WiMi Hologram Cloud Inc. (NASDAQ: WIMI), a top provider of hologram augmented reality (“AR”) technology with years of experience in holographic sights 3D map construction in the SLAM field.
Passive navigation is a technological use of self-cruising positioning, according to WiMi’s HV-SLAM. As the foundation for such powers—knowing where you are, understanding your surroundings, and consequently knowing what to do next on your own—HV-SLAM is essential to the mobility and interaction capabilities of intelligent devices like drones. One could argue that any intelligent body with mobility possesses a SLAM system of some kind.
WiMi’s HV-SLAM uses a depth camera to capture its images. An infrared camera, a DOE, and a laser projector make up the depth camera’s three main parts. They play a crucial part in assisting the system in creating a 3D holographic map that will help the machine decide on its course of action and how to move intelligently. As an illustration, the system will need to carry out the following actions when people enter a new environment and want to quickly acclimatize to it and finish tasks.
Feature extraction is the process of using the sensor to gather data about the environment and the surrounding objects.
Map construction: A 3D holographic map of the environmental features is created in the system based on the data collected by the Sensor.
Dynamic calibration and adjustment: As the system moves, it continuously acquires new feature landmarks and corrects its 3D holographic map model.
Annotating a trajectory involves locating a point using feature landmarks gathered during the previous movement period.
Checking whether the loops can be safely matched and returned is known as loop closure detection.
The HV-SLAM algorithm tracks the location and orientation of the camera while creating a real-time 3D holographic world map. The system is able to do self-correcting passive location navigation by merging CNNs with deep learning. Although deep learning perceives and recognizes things in order to provide guidance, HV-SLAM focuses on geometry and space. The use of computer vision technologies to visual SLAM allows for considerable efficiency gains by identifying, characterizing, and matching prominent characteristics, picture identification, retrieval, etc. The HV-SLAM technology from WiMi aids machines in geometrically comprehending the environment around them by creating associations in the local coordinate system. The gadget can do classification inference, or create relationships between various object instances, thanks to the deep learning engine.
There are some algorithms in WiMi’s HV-SLAM that also need to be taken into account, continually improved, and upgraded. The next steps in the advancement of SLAM technology are multi-sensor fusion, efficient data association, loopback detection, integration with heterogeneous front-end processors, increased resilience, and repositioning precision. Yet, with consumer stimulation and the growth of the industry chain, issues will progressively be remedied.
