Jon Glenn Omholt Gjevestad

In airborne surveying, light detection and ranging (LiDAR) strip adjustment and image bundle adjustment are customarily performed as separate processes. The bundle adjustment is usually conducted from frame images, while using linear pushbroom (LP) images in the bundle adjustment has been historically challenging due to the limited number of observations available to estimate the exterior image orientations. However, data from these three sensors conceptually provide information to estimate the same trajectory corrections, which is favorable for solving the problems of image depth estimation or the planimetric correction of LiDAR point clouds. Thus, our purpose with the presented study is to jointly estimate corrections to the trajectory and interior sensor states in a scalable hybrid adjustment between 3D LiDAR point clouds, 2D frame images, and 1D LP images. Trajectory preprocessing is performed before the low-frequency corrections are estimated for certain time steps in the following adjustment using cubic spline interpolation. Furthermore, the voxelization of the LiDAR data is used to robustly and efficiently form LiDAR observations and hybrid observations between the image tie-points and the LiDAR point cloud to be used in the adjustment. The method is successfully demonstrated with an experiment, showing the joint adjustment of data from the three different sensors using the same trajectory correction model with spline interpolation of the trajectory corrections. The results show that the choice of the trajectory segmentation time step is not critical. Furthermore, photogrammetric sub-pixel planimetric accuracy is achieved, and height accuracy on the order of mm is achieved for the LiDAR point cloud. This is the first time these three types of sensors with fundamentally different acquisition techniques have been integrated. The suggested methodology presents a joint adjustment of all sensor observations and lays the foundation for including additional sensors for kinematic mapping in the future.

SUMMARY: Since the beginning of the 1990s, absolute gravity has been observed at several locations in Norway with FG5-type instruments to investigate the temporal gravity changes due to glacial isostatic adjustment (GIA). Previous work suggests that some of the estimated secular gravity trends may be affected by remaining unmodelled geophysical effects, such as the effect of local hydrology. In this work, we compute hydrological gravity effects from global hydrological models for the far zone and a combination of regional run-off models and modelling of residual hydrological effects for the near zone. The method developed in this paper is first tested at the geodetic observatory in Wettzell, Germany, where both high-resolution superconducting gravimeter data as well as a regional hydrological model are available. Next, the method is transferred to two Norwegian gravity sites (NMBU and TRYC), with long time-series of frequent absolute gravity observations using FG5-226. At these sites, we investigate the impact of the hydrological gravity correction on data variability as well as estimated secular gravity trends. We find that the data variability is reduced by up to 40 per cent when applying the modelled hydrological gravity effect at TRYC. The reduction is less at NMBU where the amplitude of the hydrological signal and in consequence also the signal-to-noise ratio are smaller. We also note that it is challenging to determine the near zone residual hydrological effects without carefully taking into account the hydrogeological setting of the area for modelling such effects. When utilizing the long corrected absolute gravity series to determine the gravity trends, we find that the estimated trends are not significantly different from the uncorrected observations. However, the uncertainty of the estimated trends is reduced significantly for a limited corrected gravity series. We further suggest repeatability analysis of the absolute gravimeter. From the long time-series at NMBU we find a significant step coinciding with hardware replacement. We find that the fit between observed and modelled secular trends improves when introducing the step. Further analysis of gravity rates at other stations is needed to verify the existence of a real instrumental offset.

The AdMos receiver from Advanced Sport Instruments is a global navigation satellite system (GNSS) frequently used in alpine ski racing, with users from national and professional teams. Therefore, a validation was conducted for use of the AdMos in alpine skiing, using data from both recreational and competitive skiers. Athletes skied a total of 60 km in different measurement and skiing conditions, while carrying both an AdMos and a differential GNSS, which was used as the gold standard. From the GNSS position data, speed, acceleration, turn radius, trajectory incline and impulse were calculated as instantaneous and turn average measures for both GNSS systems and errors between the systems were calculated. The median and interquartile range (IQR) for the instantaneous errors were below 3.5 (3.5) m for horizontal plane position and below 7.0 (4.3) m for the 3D position. The median and IQR for instantaneous errors and turn average errors, respectively, were below 0.04 (0.24)/0.04 (0.16) m/s for speed, below 0.23 (1.06)/0.35 (0.63) m/s² for acceleration, below 0.47 (5.65)/0.73 (5.3) m for turn radius, and below 0.043 (1.96)/0.42 (1.42) degrees for trajectory incline. The median and IQR for turn average impulse were 0.025 (0.099) BWs. The position error changed gradually and randomly over time, with low noise levels causing smooth trajectories of similar shape but spatially shifted from the true trajectory that allowed the position–time derivation of the performance parameters, and detection of turns with 3% median and 5% IQR error. The accuracy assessment revealed that (1) the error levels were comparable to other consumer-grade standalone GNSS units designed for sport; (2) the trajectories closely resembled the true trajectories but with a random shift that changed over time and had a low noise level; (3) there was a very low instantaneous speed error that may allow the detection of many performance aspects of skiing and other sports; and (4) there were larger instantaneous errors for the remaining performance parameters, which decreased substantially when averaged over a turn.

Automated robotic platforms are an important part of precision agriculture solutions for sustainable food production. Agri-robots require robust and accurate guidance systems in order to navigate between crops and to and from their base station. Onboard sensors such as machine vision cameras offer a flexible guidance alternative to more expensive solutions for structured environments such as scanning lidar or RTK-GNSS. The main challenges for visual crop row guidance are the dramatic differences in appearance of crops between farms and throughout the season and the variations in crop spacing and contours of the crop rows. Here we present a visual guidance pipeline for an agri-robot operating in strawberry fields in Norway that is based on semantic segmentation with a convolution neural network (CNN) to segment input RGB images into crop and not-crop (i.e., drivable terrain) regions. To handle the uneven contours of crop rows in Norway’s hilly agricultural regions, we develop a new adaptive multi-ROI method for fitting trajectories to the drivable regions. We test our approach in open-loop trials with a real agri-robot operating in the field and show that our approach compares favourably to other traditional guidance approaches.

There is a need to quantify and better understand how wildlife interact with linear features, as these are integral elements of most landscapes. One potentially important aspect is linear feature tracking (LFT), yet studies rarely succeed in directly revealing or quantifying this behavior. In a proof-of-concept study, we employed short-term intensive GPS monitoring of red foxes (Vulpes vulpes) in a multiple-use landscape in southern Norway. Using periodic bursts of high frequency GPS position fixes, we performed modified path selection analyses to estimate the propensity of foxes to track natural and man-made linear features (roads, forest edges, and streams) once they are encountered. Foxes in our study tracked primarily forest edges and roads. Forty-three percent of bursts that encountered any linear feature resulted in LFT. LFT, although prominent, was manifested as a short-lived behavior, with overall median times to linear feature abandonment around two minutes. Movement speeds were highest along roads, perhaps due to greater ease of travel or higher perceived risk. In the highly heterogeneous habitats that characterize human-dominated landscapes, LFT may be manifested at such a fine spatio-temporal scale that it would remain hidden during telemetry studies employing conventional position fix frequencies. The approach described here may aid others studying spatial behaviors that are manifested over very short durations, yet are biologically significant.

We present a comparative study of computational methods for estimation of ionospheric scintillation indices. First, we review the conventional approaches based on Fourier transformation and low-pass/high-pass frequency filtration. Next, we introduce a novel method based on nonparametric local regression with bias Corrected Akaike Information Criteria (AICC). All methods are then applied to data from the Norwegian Regional Ionospheric Scintillation Network (NRISN), which is shown to be dominated by phase scintillation and not amplitude scintillation. We find that all methods provide highly correlated results, demonstrating the validity of the new approach to this problem. All methods are shown to be very sensitive to filter characteristics and the averaging interval. Finally, we find that the new method is more robust to discontinuous phase observations than conventional methods.