Evaluations on multi-scale camera networks for precise and geo-accurate reconstructions from aerial and terrestrial images with user guidance

Rumpler, Markus; Tscharf, Alexander; Mostegel, Christian; Daftry, Shreyansh; Hoppe, Christof; Prettenthaler, Rudolf; Fraundorfer, Friedrich; Mayer, Gerhard; Bischof, Horst

doi:10.1016/j.cviu.2016.04.008

Cited by 31 publications

(19 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…UAVs-based environmental studies are gaining space in recent years. The recent developments in UAVs and in the associated image-processing techniques extend the UAVs application fields, such as disaster monitoring (Niethammer et al 2012), topography (Rumpler et al 2016;Turner, Harley, and Drummond 2016), archaeology (Alasino et al 2012;Bendeaa et al 2007;Remondino et al 2011), glacier (Bhardwaj et al 2016), soil erosion (Peter et al 2014), precision agriculture (Comba et al 2015;Gago et al 2015;Pérez-Ortiz et al 2016;Rokhmana 2015;Schellberg et al 2008), resource management (Nishar et al 2016), and so on.…”

Section: Introductionmentioning

confidence: 99%

A physical-based atmospheric correction algorithm of unmanned aerial vehicles images and its utility analysis

Liu

et al. 2016

International Journal of Remote Sensing

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs)-based environmental studies are gaining space in recent years due to their advantages of minimal cost, flexibility, and very high spatial resolution. Researchers can acquire imagery according to their schedule and convenience with the option of alternating the sensors working in visible, infrared, and microwave wavelengths. The recent developments in UAVs and in the associated image-processing techniques extend the fields of UAVs application. Inherent geometric deformation of UAVs images inevitably leads to burgeoning interest in exploring the geographical registration techniques of UAVs images preprocessing. However, atmospheric correction had been generally neglected due to the low altitudes of UAVs platforms. The path radiance of low-latitude atmosphere misleads the reflectance of target objects. Thus, a valid atmospheric correction is essential in the cases where vegetation indices (VIs) are adopted in vegetation monitoring. The off-the-shelf atmospheric correction algorithms adopted in satellite-based remote sensing are typically ill-suited for UAVs-based images due to the distinctly different altitudes and radiation transfer modes. This article identified the effect of atmospheric attenuation for spectral data collected by UAVs sensors of different altitudes and developed a physical-based atmospheric correction algorithm of UAVs images. Field-measured reflectance spectrum was essential in modelling. A sunny and dry day and a flat terrain were the two prerequisites to ensure the general application of the developed algorithm. A case study was subsequently carried out to verify the utility of the developed algorithm, and the results showed that VIs based on the UAVs images of different altitudes had a similar ability in vegetation assessment as groundbased recordings. However, the assessment accuracy could be clearly improved by using the developed atmospheric correction algorithm. ARTICLE HISTORY

show abstract

Section: Introductionmentioning

confidence: 99%

A physical-based atmospheric correction algorithm of unmanned aerial vehicles images and its utility analysis

Liu

et al. 2016

International Journal of Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Following the method (Rumpler et al, , ), the GPS measurements and the camera poses of the sparse model are aligned by means of a robust random sample consensus (RANSAC)‐based least‐squares minimization of the distance between both sets of locations. This effectively provides the correct scale, rotation, and relative translation for the sparse 3D map.…”

Section: Overview Obstacle Map Generationmentioning

confidence: 99%

“…Our vision‐based mapping approach consists of two separate steps. First, the drone performs an overview flight at a safe altitude acquiring overlapping downward‐looking images, while creating a high‐quality map of the environment by using a state‐of‐the‐art photogrammetry method, the online Structure from Motion (SfM) pipeline (Hoppe et al, ; Rumpler et al, ). Second, this map is georeferenced and converted into an Octomap, see Figure , which is used as an initial overview obstacle map that can be updated during the rest of the flight while performing a task of interest near the ground.…”

Section: Introductionmentioning

confidence: 99%

Overview obstacle maps for obstacle‐aware navigation of autonomous drones

Pestana

Maurer

Muschick

et al. 2019

Journal of Field Robotics

Self Cite

View full text Add to dashboard Cite

Achieving the autonomous deployment of aerial robots in unknown outdoor environments using only onboard computation is a challenging task. In this study, we have developed a solution to demonstrate the feasibility of autonomously deploying drones in unknown outdoor environments, with the main capability of providing an obstacle map of the area of interest in a short period of time. We focus on use cases where no obstacle maps are available beforehand, for instance, in search and rescue scenarios, and on increasing the autonomy of drones in such situations. Our vision‐based mapping approach consists of two separate steps. First, the drone performs an overview flight at a safe altitude acquiring overlapping nadir images, while creating a high‐quality sparse map of the environment by using a state‐of‐the‐art photogrammetry method. Second, this map is georeferenced, densified by fitting a mesh model and converted into an Octomap obstacle map, which can be continuously updated while performing a task of interest near the ground or in the vicinity of objects. The generation of the overview obstacle map is performed in almost real time on the onboard computer of the drone, a map of size 100m×75m is created in ≈2.75min, therefore, with enough time remaining for the drone to execute other tasks inside the area of interest during the same flight. We evaluate quantitatively the accuracy of the acquired map and the characteristics of the planned trajectories. We further demonstrate experimentally the safe navigation of the drone in an area mapped with our proposed approach.

show abstract

“…In our case, bundle adjustment (BA) is carried out based on Google's Ceres Solver library for non-linear least squares problems (Agarwal et al, 2018). As in Rumpler et al (2017), our BA procedure (see Equation 1) not only minimizes the reprojection errors between the projected natural 3D points as well as ground control points and its corresponding 2D measurements in image space (see Equation 2), but it also minimizes differences of 3D projection center coordinates from direct georeferencing and photogrammetric reconstruction (see Equation 3). Furthermore, we use the robust Cauchy function to potentially down-weight outliers.…”

Section: Global Reconstructionmentioning

confidence: 99%

“…Integrated georeferencing is also necessary indoors due to the limited accuracy of online SLAM, which is the predominant pose estimation procedure for indoor mapping systems (Lehtola et al, 2017;Nocerino et al, 2017). Rumpler et al (2017) developed a complete processing pipeline from image capturing to mesh generation. They consider weighted ground control points (GCP) and GNSS positions in the final bundle adjustment.…”

Section: Introductionmentioning

confidence: 99%

Robust and Accurate Image-Based Georeferencing Exploiting Relative Orientation Constraints

Cavegn

Blaser

Nebiker

et al. 2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Urban environments with extended areas of poor GNSS coverage as well as indoor spaces that often rely on real-time SLAM algorithms for camera pose estimation require sophisticated georeferencing in order to fulfill our high requirements of a few centimeters for absolute 3D point measurement accuracies. Since we focus on image-based mobile mapping, we extended the structure-from-motion pipeline COLMAP with georeferencing capabilities by integrating exterior orientation parameters from direct sensor orientation or SLAM as well as ground control points into bundle adjustment. Furthermore, we exploit constraints for relative orientation parameters among all cameras in bundle adjustment, which leads to a significant robustness and accuracy increase especially by incorporating highly redundant multi-view image sequences. We evaluated our integrated georeferencing approach on two data sets, one captured outdoors by a vehicle-based multi-stereo mobile mapping system and the other captured indoors by a portable panoramic mobile mapping system. We obtained mean RMSE values for check point residuals between image-based georeferencing and tachymetry of 2 cm in an indoor area, and 3 cm in an urban environment where the measurement distances are a multiple compared to indoors. Moreover, in comparison to a solely image-based procedure, our integrated georeferencing approach showed a consistent accuracy increase by a factor of 2-3 at our outdoor test site. Due to pre-calibrated relative orientation parameters, images of all camera heads were oriented correctly in our challenging indoor environment. By performing self-calibration of relative orientation parameters among respective cameras of our vehicle-based mobile mapping system, remaining inaccuracies from suboptimal test field calibration were successfully compensated.

show abstract

Evaluations on multi-scale camera networks for precise and geo-accurate reconstructions from aerial and terrestrial images with user guidance

Cited by 31 publications

References 30 publications

A physical-based atmospheric correction algorithm of unmanned aerial vehicles images and its utility analysis

A physical-based atmospheric correction algorithm of unmanned aerial vehicles images and its utility analysis

Overview obstacle maps for obstacle‐aware navigation of autonomous drones

Robust and Accurate Image-Based Georeferencing Exploiting Relative Orientation Constraints

Contact Info

Product

Resources

About