Dragonfly is Onit’s reducing-edge computer imaginative and prescient indoor localization expertise primarily based on visible SLAM that provides correct indoor position and orientation to forklifts, automated guided autos (AGV), autonomous cellular robots robots (AMR), robots, drones and any other moving vehicle and asset. Dragonfly enables RTLS solutions for analytics, productiveness and security in GPS-denied environments like warehouses, production plants, factories and many others.. Dragonfly delivers the X-Y-Z coordinates and 3D orientation of any transferring machine with centimeter-level accuracy, by analyzing in real-time the video stream coming from a normal huge-angle digicam connected to a small computing unit. Dragonfly represents the state-of-the-art for indoor localization applied sciences at areas where GPS/GNSS can't be used and it is far more aggressive compared to other indoor localization applied sciences primarily based on LiDAR, Ultra Wide Bandwidth, Wi-Fi, Bluetooth RSS. HOW DOES IT WORK? Throughout the system setup phase the extensive-angle digital camera sends the video feed of its surroundings to the computing unit.

The computing unit takes care of extracting the options of the atmosphere, anti-loss gadget in each of the frames, and making a 3D map of the environment (which is geo-referenced using a DWG file of the realm). During its utilization in production the huge-angle camera sends the real-time video feed of its surroundings to the computing unit. The computing unit extracts the features of the environment in each of the frames and compare them with those inside the beforehand created 3D map of the atmosphere. This course of permits Dragonfly to calculate at more than 30 Hz the X-Y-Z place and orientation in the 3D area of the camera (and thus of the mobile asset on which it is mounted). Dragonfly is an accurate indoor location system primarily based on laptop imaginative and prescient. The location is computed in real time using just an on board camera and a computing unit on board of the anti-loss gadget to be tracked, due to our pc vision algorithm. Computer vision, odometry and synthetic intelligence are used to create an correct system, anti-loss gadget in an effort to deliver a exact location for a number of functions.

It is a wonderful answer for the precise indoor tracking of forklifts, AGV, AMR, robots and drones (within the 3D area). Dragonfly is way more competitive than LiDAR, UWB, radio signal based mostly applied sciences for which an ad-hoc infrastructure must be designed, setup, calibrated and maintained for every specific venue. No receivers, no RFID tags, no antennas, no nodes, no magnetic stripes. Nothing needs to be deployed by the venue. You want only a digicam and a computing unit onboard your mobile vehicles. No tech abilities required, no difficult directions, buy itagpro no need for error-prone and anti-loss gadget time-consuming calibrations of ad-hoc UWB infrastructure. SLAM technology is rather more robust to environmental changes versus LiDAR, which struggles significantly to maintain accuracy in environments wherein obstacles change over time. Dragonfly cameras are easier to calibrate and are extra robust to adjustments in the surroundings. Dragonfly distributed structure makes the answer dependable by eliminating necessary server that led to SPOF (single points of failures).

This additionally implies that Dragonfly can develop following the size and anti-loss gadget progress of your fleet of transferring 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 permits you to optimize your operations, increasing the productiveness and effectiveness of the tracked gadgets. In addition to this its competitive value, makes the ROI larger than another know-how presently available on the market. Enhance the operations because of an actual-time visibility of the particular utilization and path of your mobile autos (like forklifts) to keep away from underneath/over utilization and maximize the performance of the fleet. Know in real-time the placement of each moving asset to forestall accidents between human-guided mobile automobiles (equivalent to forklifts) inside warehouses and anti-loss gadget manufacturing facilities enabling thus V2V (vehicle to vehicle) and V2P (autos to pedestrians) applications for collision-avoidance. Speed up the productivity by tracking the location of every shifting asset to indirectly know the place of every dealing with unit on the bottom, racks and shelves.