  Drones may be more advantageous than fixed cameras for quality control
applications in industrial facilities, since they can be redeployed dynamically
and adjusted to production planning. The practical scenario that has motivated
this paper, image acquisition with drones in a car manufacturing plant,
requires drone positioning accuracy in the order of 5 cm. During repetitive
manufacturing processes, it is assumed that quality control imaging drones will
follow highly deterministic periodic paths, stop at predefined points to take
images and send them to image recognition servers. Therefore, by relying on
prior knowledge about production chain schedules, it is possible to optimize
the positioning technologies for the drones to stay at all times within the
boundaries of their flight plans, which will be composed of stopping points and
the paths in between. This involves mitigating issues such as temporary
blocking of line-of-sight between the drone and any existing radio beacons;
sensor data noise; and the loss of visual references. We present a
self-corrective solution for this purpose. It corrects visual odometer readings
based on filtered and clustered Ultra-Wide Band (UWB) data, as an alternative
to direct Kalman fusion. The approach combines the advantages of these
technologies when at least one of them works properly at any measurement spot.
It has three method components: independent Kalman filtering, data association
by means of stream clustering and mutual correction of sensor readings based on
the generation of cumulative correction vectors. The approach is inspired by
the observation that UWB positioning works reasonably well at static spots
whereas visual odometer measurements reflect straight displacements correctly
but can underestimate their length. Our experimental results demonstrate the
advantages of the approach in the application scenario over Kalman fusion.
