vSLAM ACCURACY AND

DRAGONFLY PRECISION

Dragonfly, our Visual SLAM technology, provides high level of accuracy.

Dragonfly is a visual SLAM technology that uses computer vision to provide the precise location of vehicles, robots, drones, forklits and moving assets.

Dragonfly Visual SLAM provides a high level of accuracy. For this technology there are two different types of accuracy and precision to consider.

Drift

The Drift


The "drift" is the accumulated error. Typically the drift applies only for stereo-camera installations. The drift applies only to “unknown” environments, that means venues and spaces where Dragonfly has not been used before, and for which there is not yet an existing 3D map created by Dragonfly. For example, the first time you drive a forklift with a camera and Dragonfly installed, inside a warehouse, you will be exploring an unknown environment.

The drift is expressed as a percentage: this number indicates the amount of accumulated error, and can be considered as the amount of meters of error for each 100 linear meters.

  • Stereo-camera installations: Dragonfly’s drift has been verified to range between 0.6% and 1.3%. This means that if you drive a forklift with Dragonfly installed on board along a 100 meters linear path, at the end Dragonfly will report a location that can be 60 cm to 130 cm inaccurate.

  • Monocular installations: there are other ways to measure the accuracy: the Range of Confidence (ROC) in this case is not the result of the accumulated error over time. In any case, several tests in real life environments demonstrated that it is possible to approximate the accuracy of monocular cameras inside an unknown environment to 1%.



Loop Closing


The drift, and the error for monocular cameras’ systems, are however automatically corrected by Dragonfly each time there is a loop-closing. This means that each time Dragonfly recognizes an area that has already been mapped, the loop closes, and Dragonfly corrects the location and the map is also updated. Loop-closings are extremely useful to improve the overall accuracy of the system, and it is strongly recommended to perform frequent loop-closings during the initial mapping, for both monocular and stereo cameras architecture.
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Linear accuracy

Linear Accuracy
When navigating inside a known environment, therefore when Dragonfly re-locates the device inside a previously created map, the accuracy depends on the precision of the triangulation of known points (features). The linear accuracy, the radius of confidence (ROC), is typically 5-10 cm, and depends on several factors, including:

  • The quality of the camera;

  • The lightning of the environment;

  • The real-world reference;

  • The dimension of the map;

  • The camera’s calibration.


As an example, imagine a drone navigating inside a hangar that has already been “mapped” before: in this case, the ROC of the drone will be 5-10 cm.
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