Investigation on Multi-Sensor Fusion Strategies for Improved Orientation Determination in Mobile Phone Imaging Applications

Elias, Melanie; Maas, Hans‐Gerd

doi:10.5194/isprs-annals-v-1-2020-181-2020

Cited by 4 publications

(1 citation statement)

References 28 publications

(29 reference statements)

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“…Zhang et al [ 23 ] use a stereo-vision technique to determine the target relative height with respect to the UAV. In addition, the sensor technology used in UAVs is very similar to that used in today’s smartphones [ 35 ]. A yaw bias estimation algorithm is also proposed.…”

Section: Georeferencing Methods For Uav Observationsmentioning

confidence: 99%

Real-Time Georeferencing of Fire Front Aerial Images Using Iterative Ray-Tracing and the Bearings-Range Extended Kalman Filter

Santana

Cherif

Bernardino

et al. 2022

Sensors

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Although Aerial Vehicle images are a viable tool for observing large-scale patterns of fires and their impacts, its application is limited by the complex optical georeferencing procedure due to the lack of distinctive visual features in forest environments. For this reason, an exploratory study on rough and flat terrains was conducted to use and validate the Iterative Ray-Tracing method in combination with a Bearings-Range Extended Kalman Filter as a real-time forest fire georeferencing and filtering algorithm on images captured by an aerial vehicle. The Iterative Ray-Tracing method requires a vehicle equipped with a Global Positioning System (GPS), an Inertial Measurement Unit (IMU), a calibrated camera, and a Digital Elevation Map (DEM). The proposed method receives the real-time input of the GPS, IMU, and the image coordinates of the pixels to georeference (computed by a companion algorithm of fire front detection) and outputs the geographical coordinates corresponding to those pixels. The Unscented Transform B is proposed to characterize the Iterative Ray-Tracing uncertainty. A Bearings-Range filter measurement model is introduced in a sequential filtering architecture to reduce the noise in the measurements, assuming static targets. A performance comparison is done between the Bearings-Only and the Bearings-Range observation models, and between the Extended and Cubature Kalman Filters. In simulation studies with ground truth, without filtering we obtained a georeferencing Root Mean Squared Errors (RMSE) of 30.7 and 43.4 m for the rough and flat terrains respectively, while filtering with the proposed Bearings-Range Extended Kalman Filter showed the best results by reducing the previous RMSE to 11.7 and 19.8 m, respectively. In addition, the comparison of both filter algorithms showed a good performance of Bearings-Range filter which was slightly faster. Indeed, these experiments based on the real data conducted to results demonstrated the applicability of the proposed methodology for the real-time georeferencing forest fires.

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Section: Georeferencing Methods For Uav Observationsmentioning

confidence: 99%