Dragonfly is Onit’s slicing-edge pc vision indoor localization expertise based mostly on visible SLAM that gives correct indoor place and orientation to forklifts, automated guided autos (AGV), autonomous cellular robots robots (AMR), iTagPro portable robots, drones and another moving automobile and iTagPro bluetooth tracker asset. Dragonfly enables RTLS options for analytics, productiveness and security in GPS-denied environments like warehouses, manufacturing plants, factories and so on.. Dragonfly delivers the X-Y-Z coordinates and 3D orientation of any moving machine with centimeter-level accuracy, by analyzing in actual-time the video stream coming from a standard wide-angle digital camera linked to a small computing unit. Dragonfly represents the state-of-the-art for indoor ItagPro localization technologies at locations the place GPS/GNSS can't be used and it is way more aggressive compared to different indoor localization technologies based on LiDAR, Ultra Wide Bandwidth, Wi-Fi, Bluetooth RSS. HOW DOES IT WORK? Through the system setup section the large-angle digital camera sends the video feed of its surroundings to the computing unit.

The computing unit takes care of extracting the features of the environment, iTagPro locator in every of the frames, and making a 3D map of the environment (which is geo-referenced utilizing a DWG file of the realm). During its usage in manufacturing the large-angle camera sends the real-time video feed of its surroundings to the computing unit. The computing unit extracts the features of the atmosphere in each of the frames and examine them with those inside the beforehand created 3D map of the environment. This course of permits Dragonfly to calculate at more than 30 Hz the X-Y-Z position and orientation within the 3D house of the camera (and thus of the mobile asset on which it is mounted). Dragonfly is an accurate indoor location system based on computer vision. The situation is computed in real time using just an on board camera and a computing unit on board of the machine to be tracked, ItagPro because of our computer imaginative and prescient algorithm. Computer imaginative and prescient, odometry and artificial intelligence are used to create an correct system, in an effort to ship a exact location for a number of applications.

It is an excellent resolution for the precise indoor tracking of forklifts, AGV, AMR, ItagPro robots and drones (within the 3D house). Dragonfly is way more aggressive than LiDAR, ItagPro UWB, radio signal based technologies for ItagPro which an ad-hoc infrastructure have to be designed, setup, calibrated and ItagPro maintained for every specific venue. No receivers, no RFID tags, no antennas, ItagPro no nodes, no magnetic stripes. Nothing must be deployed through the venue. You need just a digital camera and a computing unit onboard your mobile vehicles. No tech skills required, no troublesome directions, no want for error-prone and ItagPro time-consuming calibrations of advert-hoc UWB infrastructure. SLAM technology is far more strong to environmental changes as opposed to LiDAR, which struggles significantly to keep up accuracy in environments by which obstacles change over time. Dragonfly cameras are simpler to calibrate and are more strong to changes within the environment. Dragonfly distributed architecture makes the solution dependable by eliminating obligatory server that led to SPOF (single factors of failures).

This also means that Dragonfly can grow following the scale and growth of your fleet of shifting automobiles. Dragonfly can work completely offline on a computing unit on board of forklift, AVG, AMR, drones, robots or on an on-premise server. Dragonfly means that you can optimize your operations, growing the productivity and effectiveness of the tracked units. In addition to this its competitive value, makes the ROI greater than another technology at present on the market. Enhance the operations due to a real-time visibility of the actual utilization and path of your mobile vehicles (like forklifts) to keep away from below/over utilization and maximize the efficiency of the fleet. Know in real-time the situation of every transferring asset to stop accidents between human-guided cellular automobiles (comparable to forklifts) inside warehouses and production amenities enabling thus V2V (automobile to automobile) and V2P (automobiles to pedestrians) functions for collision-avoidance. Speed up the productivity by monitoring the placement of each moving asset to not directly know the place of each dealing with unit on the ground, racks and shelves.

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Pub: 10 Oct 2025 07:54 UTC

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