Bundle adjustment with raw inertial observations in UAV applications

Cucci, Davide Antonio; Řehák, Martin; Skaloud, Jan

doi:10.1016/j.isprsjprs.2017.05.008

Cited by 69 publications

(52 citation statements)

References 15 publications

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“…The navigational IMU on-board the autopilot was not precise nor accurate enough with regards to time tagging to allow full DSO. The introduction of a more precise IMU -analogous to the improvement in precision 20 between SPS and PPK geolocation in this study -would allow full DSO geolocation in the SfM-MVS process (Cucci et al, 2017) using a low-cost UAV system.…”

Section: Future Directionsmentioning

confidence: 99%

High accuracy UAV photogrammetry of ice sheet dynamics with no ground control

Chudley¹,

Christoffersen²,

Doyle³

et al. 2018

Preprint

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Unmanned Aerial Vehicles (UAVs) and Structure from Motion with Multi-View Stereo (SfM-MVS) photogrammetry are increasingly common tools for geoscience applications, but final product accuracy can be significantly diminished in the absence of a dense and well-distributed network of ground control points (GCPs). This is problematic in inaccessible or hazardous field environments, including highly crevassed glaciers, where implementing suitable GCP networks would be logistically difficult if not impossible. To overcome this challenge, we present an alternative geolocation approach known 5 as GNSS-supported aerial triangulation (GNSS-AT). Here, an on-board carrier-phase GNSS receiver is used to determine the location of photo acquisitions using kinematic differential carrier-phase positioning. The camera positions can be used as the geospatial input to the photogrammetry process. We describe the implementation of this method in a low-cost, custom-built UAV, and apply the method in a glaciological setting at Store Glacier in West Greenland. We validate the technique at the calving front, achieving topographic uncertainties of ±0.07 m horizontally and ±0.14 m vertically when flying at an altitude 10 of ∼450 m a.s.l. This compares favourably with previous GCP-derived uncertainties in glacial environments, and allowed us to apply the SfM-MVS photogrammetry at an inland study site where ice flows at 2 m day -1 and where stable ground control is not available. Here, we were able to produce, without the use of GCPs, the first UAV-derived velocity fields of an ice sheet interior. Given the growing use of UAVs and SfM-MVS in glaciology and the geosciences, GNSS-AT will be of interest to those wishing to use UAV photogrammetry to obtain high-precision measurements of topographic change in contexts where 15

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Section: Future Directionsmentioning

confidence: 99%

High accuracy UAV photogrammetry of ice sheet dynamics with no ground control

Chudley¹,

Christoffersen²,

Doyle³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Our solutions to both of these problems are described in [22]. However, the boresight from frame camera to IMU can be also be estimated using recently developed methods involving the use of raw inertial measurements [23]. Alongside the optimisation, the bundle adjustment allows to build and export an orthophoto and a DEM ( Figure 3).…”

Section: Bundle Adjustment Of Frame Imagesmentioning

confidence: 99%

Pushbroom Hyperspectral Data Orientation by Combining Feature-Based and Area-Based Co-Registration Techniques

Barbieux

2018

Remote Sensing

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Direct georeferencing of airborne pushbroom scanner data usually suffers from the limited precision of navigation sensors onboard of the aircraft. The bundle adjustment of images and orientation parameters, used to perform geocorrection of frame images during the post-processing phase, cannot be used for pushbroom cameras without difficulties-it relies on matching corresponding points between scan lines, which is not feasible in the absence of sufficient overlap and texture information. We address this georeferencing problem by equipping our aircraft with both a frame camera and a pushbroom scanner: the frame images and the navigation parameters measured by a couple GPS/Inertial Measurement Unit (IMU) are input to a bundle adjustment algorithm; the output orientation parameters are used to project the scan lines on a Digital Elevation Model (DEM) and on an orthophoto generated during the bundle adjustment step; using the image feature matching algorithm Speeded Up Robust Features (SURF), corresponding points between the image formed by the projected scan lines and the orthophoto are matched, and through a least-squares method, the boresight between the two cameras is estimated and included in the calculation of the projection. Finally, using Particle Image Velocimetry (PIV) on the gradient image, the projection is deformed into a final image that fits the geometry of the orthophoto. We apply this algorithm to five test acquisitions over Lake Geneva region (Switzerland) and Lake Baikal region (Russia). The results are quantified in terms of Root Mean Square Error (RMSE) between matching points of the RGB orthophoto and the pushbroom projection. From a first projection where the Interior Orientation Parameters (IOP) are known with limited precision and the RMSE goes up to 41 pixels, our geocorrection estimates IOP, boresight and Exterior Orientation Parameters (EOP) and produces a new projection with an RMSE, with the reference orthophoto, around two pixels.

show abstract

“…Our solutions to both of these problems are described in [21]. However, the boresight from frame camera to IMU can be also be estimated using recently developed methods involving the use of raw inertial measurements [22]. Alongside the optimisation, the bundle adjustment allows to build and export an orthophoto and a DEM ( Figure 3).…”

Section: Bundle Adjustment Of Frame Imagesmentioning

confidence: 99%

Pushbroom Hyperspectral Data Orientation by Combining Feature-Based and Area-Based Co-Registration Techniques

Barbieux¹

2018

Preprint

View full text Add to dashboard Cite

Direct georeferencing of airborne pushbroom scanner data usually suffers from the limited precision of navigation sensors onboard of the aircraft. The bundle adjustment of images and orientation parameters, used to perform geocorrection of frame images during the post-processing phase, cannot be used for pushbroom cameras without difficulties: it relies on matching corresponding points between scan lines, which is not feasible in the absence of sufficient overlap and texture information. We address this georeferencing problem by equipping our aircraft with both a frame camera and a pushbroom scanner: the frame images and the navigation parameters measured by a couple GPS/Inertial Measurement Unit (IMU) are input to a bundle adjustment algorithm; the output orientation parameters are used to project the scan lines on a Digital Elevation Model (DEM) and on an orthophoto generated during the bundle adjustement step; using the image feature matching algorithm Speeded Up Robust Features (SURF), corresponding points between the image formed by the projected scan lines and the orthophoto are matched, and through a least-squares method, the boresight between the two cameras is estimated and included in the calculation of the projection; finally, using Particle Image Velocimetry (PIV) on the gradient image, the projection is deformed into a final image that fits the geometry of the orthophoto. We apply this algorithm to five test acquisitions over Lake Geneva region (Switzerland) and Lake Baikal region (Russia). The results are quantified in terms of Root Mean Square Error (RMSE) between matching points of the RGB orthophoto and the pushbroom projection. From a first projection where the Interior Orientation Parameters (IOP) are known with limited precision and the RMSE goes up to 41 pixels, our geocorrection estimates IOP, boresight and Exterior Orientation Parameters (EOP) and produces a new projection which RMSE with the reference orthophoto is around two pixels.

show abstract

Bundle adjustment with raw inertial observations in UAV applications

Cited by 69 publications

References 15 publications

High accuracy UAV photogrammetry of ice sheet dynamics with no ground control

High accuracy UAV photogrammetry of ice sheet dynamics with no ground control

Pushbroom Hyperspectral Data Orientation by Combining Feature-Based and Area-Based Co-Registration Techniques

Pushbroom Hyperspectral Data Orientation by Combining Feature-Based and Area-Based Co-Registration Techniques

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