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We have recently released in the open domain data originating from a series of flights conducted with a fixed-wing micro UAV carrying high-quality small camera and navigation sensors. This data was previously used in several peer-reviewed publications. However, the data that we describe in the following is part of a larger series that will be released gradually after incorporating user feedback (e.g., on formats, description, etc.) from the first (three) released open data-sets. In the first part of this work we provide a thorough description of the common elements of these data sets, notably the UAV, its sensors, methods of time-stamping and synchronizing data streams, reference geometrical relations among them (system calibration) as well as time-invariant sensor parameters (e.g., lens distortion, non-orthogonality of inertial sensors) together with ground control points that are valid over the whole series. In the second part we describe the individual missions and provide the links to the released data sets.

Photogrammetry and Laser-Scanning are usually considered as complementary. Integration of these two observation methods has the potential to blend their individual advantages. The resulting benefit is likely to be higher in drone airborne mapping, which payload capacity (and thus the quality of the embedded IMU) is limited. Thus, the trajectory computed by the IMU is subject to important time-dependent errors: even if the global attitude is less adequate, it is self-coherent locally. For this reason, we propose a close integration of Photogrammetry with Laser-Scanning based on the correction of time-dependent error of the trajectory with the help of the image observations acquired by the camera. Apart from the trajectory, this hybridization requires optical correspondences between image and Laser measurements. Such full set of input data is rigorously fused together in a Bundle-Adjustment in order to better determine the trajectory, and thus the resulting point-cloud. The presented theory was practically evaluated in an airborne case against a reference solution.

Dynamic Networks have been introduced in the literature to solve multi-sensor fusion problems for navigation and mapping. They have been shown to outperform conventional methods in challenging scenarios, such as corridor mapping or self-calibration. In this work we investigate the problem of how raw inertial readings can be fused with GNSS position observations in Dynamic Networks (DN) with the goal of i) limiting the number of unknowns in the estimation problem and ii) improving the conditioning of the normal equations arising in least-squares adjustments in the absence of spatial constraints (e.g., image observations). For that we propose a modified version of the well known IMU-preintegration method, accounting for a non-constant gravity model, the Earth rotation and the apparent Coriolis force, and we compare it with the conventional DN formulation in a emulated scenario. This consists of a fixed-wing UAV flying four times over a 2 km long corridor.

Autonomous navigation is a crucial aspect for fixed-wing unmanned aerial vehicle operation when encountering disturbance or denial of GNSS signal reception. Vehicle dynamic model - based navigation improves the performance of methods based exclusively on introspective sensors (e.g., inertial, pressure) by incorporating mathematical models of aerodynamic forces and moments into the final navigation solution. Such an approach, however, requires model-coefficients that are calibrated to the specific vehicle being operated. We propose augmenting the self-calibration of these coefficients with additional observations derived from an on-board camera via photogrammetry. We confirm experimentally that irregular yet precise observations of vehicle absolute attitude obtained through such techniques greatly improve the determination of some aerodynamic coefficients that in turn reduce the error in autonomous positioning under GNSS signal outage.

The UAV mapping industry expanded tremendously during the last five years. Thanks to miniaturization, automation and advertising, this technology may give a wrong impression that mapping of certain quality is as simple as clicking few buttons on a PC. Moreover, with a large and continuously increasing offer of hardware and software, the identification of the right tools is not easy, especially when aiming at certain standard. In this respect, the mapping with LiDAR is more delicate than with a camera due to a lower level of redundancy within the process of orientation/georeferencing and somewhat higher threshold on the size/weight per performance ratio within these sensors. This fact motivated us to present a practical benchmark evaluating a popular small LiDAR sensor in realistic conditions for intrinsic parameters such as noise or capacity to penetrate canopy, as well as the “low-weight” inertial technology in terms of geometrical influences on the resulting point cloud. The practical limitations are indeed considerably lower than those specified by the manufacturers or tested in laboratory conditions. These should be considered together with other “mapping-productivity” factors that are summarized in the last part of this study.

Unmanned Aerial Vehicles (UAVs) have benefited from a tremendous increase in popularity over the past decade, which has inspired their application toward many novel and unique use cases. One of them is the use of UAVs in meteorological research, in particular for wind measurement. Research in this field using quadcopter UAVs has shown promising results. However, most of the results in the literature suffer from three main drawbacks. First, experiments are performed as numerical simulations or in wind tunnels. Such results are limited in their validity in real-life conditions. Second, it is almost always assumed that the drone is stationary, which limits measurements spatially. Third, no attempts at estimating vertical wind are made. Overcoming these limitations offer an opportunity to gain significant value from using UAVs for meteorological measurements. We address these shortcomings by proposing a new dynamic model-based approach, that relies on the assumption that thrust can be measured or estimated, while drag can be related to air speed. Moreover, the proposed method is tested on empirical data gathered on a DJI Phantom 4 drone. During hovering, our method leads to precision and accuracy comparable to existing methods that use tilt to estimate the wind. At the same time, the method is able to estimate wind while the drone is moving. This paves the way for new uses of UAVs, such as the measurement of shear wind profiles, knowledge of which is relevant in Atmospheric Boundary Layer (ABL) meteorology. Additionally, since a commercial off-the-shelf drone is used, the methodology can be replicated by others without any need for custom hardware development or modifications.

In many fields, going from economics to physics, it is common to deal with measurements that are taken in time. These measurements are often explained by known external factors that describe a large part of their behavior. For example, the evolution of the unemployment rate in time can be explained by the behavior of the gross domestic product (the external factor in this case). However, in many cases the external factors are not enough to explain the entire behavior of the measurements, and it is necessary to use so-called stochastic models (or probabilistic models) that describe how the measurements are dependent on each other through time (i.e., the measurements are explained by the behavior of the previous measurements themselves). The treatment and analysis of the latter kind of behavior is known by various names, such as timeseries analysis or signal processing. In the majority of cases, the goal of this analysis is to estimate the parameters of the underlying models which, in some sense, explain how and to what extent the observations depend on each other through time.

It is well known that accurate aerial position and attitude control is beneficial for image orientation in airborne photogrammetry. The aerial control is traditionally obtained by Kalman filtering/smoothing inertial and GNSS observations prior to the bundle-adjustment. However, in Micro Aerial Vehicles this process may result in poor attitude determination due to the limited quality of the inertial sensors, large alignment uncertainty and residual correlations between sensor biases and initial attitude. We propose to include the raw inertial observations directly into the bundle-adjustment instead of as position and attitude weighted observations from a separate inertial/GNSS fusion step. The necessary observation models are derived in detail within the context of the so called “Dynamic Networks”. We examine different real world cases and we show that the proposed approach is superior to the established processing pipeline in challenging scenarios such as mapping in corridors and in areas where the reception of GNSS signals is denied.

Mapping with Micro Aerial Vehicles (MAVs whose weight does not exceed 5 kg) is gaining importance in applications such as corridor mapping, road and pipeline inspections, or mapping of large areas with homogeneous surface structure, e.g. forest or agricultural fields. In these challenging scenarios, integrated sensor orientation (ISO) improves effectiveness and accuracy. Furthermore, in block geometry configurations, this mode of operation allows mapping without ground control points (GCPs). Accurate camera positions are traditionally determined by carrier-phase GNSS (Global Navigation Satellite System) positioning. However, such mode of positioning has strong requirements on receiver’s and antenna’s performance. In this article, we present a mapping project in which we employ a single-frequency, low-cost (

This article discusses the problem of time registration between navigation and imaging components on Micro Aerial Vehicles (MAVs). Accurate mapping with MAVs is gaining importance in applications such as corridor mapping, road and pipeline inspections or mapping of large areas with homogeneous surface structure, e.g. forests or agricultural fields. Therefore, accurate aerial control plays a major role in efficient reconstruction of the terrain and artifact-free ortophoto generation. A key prerequisite is correct time stamping of images in global time frame as the sensor exterior orientation changes rapidly and its determination by navigation sensors influence the mapping accuracy on the ground. A majority of MAVs is equipped with consumer-grade, non-metric cameras for which the precise time registration with navigation components is not trivial to realize and its performance not easy to assess. In this paper, we study the problematic of synchronization by implementing and evaluating spatio-temporal observation models of aerial control to estimate residual delay of the imaging sensor. Such modeling is possible through inclusion of additional velocity and angular rate observations into the adjustment. This moves the optimization problem from 3D to 4D. The benefit of this approach is verified on real mapping projects using a custom build MAV and an off-the-shelf camera.

The task of inertial sensor calibration has required the development of various techniques to take into account the sources of measurement error coming from such devices. The calibration of the stochastic errors of these sensors has been the focus of increasing amount of research in which the method of reference has been the so-called “Allan variance (AV) slope method” which, in addition to not having appropriate statistical properties, requires a subjective input which makes it prone to mistakes. To overcome this, recent research has started proposing “automatic” approaches where the parameters of the probabilistic models underlying the error signals are estimated by matching functions of the AV or wavelet variance with their model-implied counterparts. However, given the increased use of such techniques, there has been no study or clear direction for practitioners on which approach is optimal for the purpose of sensor calibration. This article, for the first time, formally defines the class of estimators based on this technique and puts forward theoretical and applied results that, comparing with estimators in this class, suggest the use of the Generalized method of Wavelet moments (GMWM) as an optimal choice. In addition to analytical proofs, experiment-driven Monte Carlo simulations demonstrated the superior performance of this estimator. Further analysis of the error signal from a gyroscope was also provided to further motivate performing such analyses, as real-world observed error signals may show significant deviation from manufacturer-provided error models.

Proper modeling of stochastic errors in inertial sensors plays a crucial role in the achievable quality of GNSS-INS integration especially with low-cost inertial sensors. Generalized Method of Wavelet Moments (GMWM) can model the underlying process for such errors with arbitrarily complex structure to obtain close match to the observed errors in terms of wavelet variance. In comparison to the widely used and IEEE adopted error modeling using Allan Variance, this method provides consistent estimation, ability to estimate parameters of composite stochastic models of much higher complexities, and considerably easier usage. However, the level of improvement in navigation quality does not necessarily grow proportionally to the fidelity of the error models. Therefore, opting for unnecessarily complex models may only increase computational load with no tangible gain in navigation quality. On the other hand, converging estimation of higher complexity models, generally speaking, requires longer estimation periods and higher dynamics to improve observability of error states. This implies yet another motivation to find an optimal sensor error model avoiding unnecessary complexities. In this paper, we employ two methods to investigate the effect of model complexity on integrated navigation performance. Firstly, a covariance propagation is performed on static conditions, as is the standard scenario in error analysis of inertial navigation systems. Afterwards, an emulation study is performed based on a real Unmanned Aerial Vehicle (UAV) flight and error signals of a Navchip V2 Inertial Measurement Unit (IMU). Results of both methods are in general agreement, and suggest that more complex models in general provide higher accuracy for the navigation system and a more consistent covariance prediction by the navigation filter. This difference is, however, more noticable only in GNSS outages of longer duration (tens of seconds). However, the benefits of more complex models may be only marginal in other applications, depending on the duration of inertial coasting and availability of other sensory data such as GNSS observations.

This paper presents a novel approach to autonomous navigation for small UAVs, with no extra sensor added to the conventional INS/GNSS setup. The proposed method significantly increases the accuracy and reliability of autonomous navigation, especially for small UAVs with low-cost IMUs. This improvement is of special interest in the case of GNSS outages, where inertial coasting drifts very quickly. In the proposed architecture, the VDM provides the estimate of position, velocity, and attitude, which is updated within a filter based on available observations, such as IMU data or when available, GNSS measurements. The filter is capable of estimating wind velocity and dynamic model parameters, in addition to navigation states and IMU sensor errors. Monte Carlo simulations reveal major improvements in navigation accuracy compared to conventional INS/GNSS systems during GNSS outages of 5 min. A discussion on the observability is also presented at the end. Copyright (C) 2016 Institute of Navigation.

Advances in unmanned aerial vehicles (UAV) and especially micro aerial vehicle (MAV) technology together with increasing quality and decreasing price of imaging devices have resulted in growing use of MAVs in photogrammetry. The practicality of MAV mapping is seriously enhanced with the ability to determine parameters of exterior orientation (EO) with sufficient accuracy, in both absolute and relative senses (change of attitude between successive images). While differential carrier phase GNSS satisfies cm-level positioning accuracy, precise attitude determination is essential for both direct sensor orientation (DiSO) and integrated sensor orientation (ISO) in corridor mapping or in block configuration imaging over surfaces with low texture. Limited cost, size, and weight of MAVs represent limitations on quality of onboard navigation sensors and puts emphasis on exploiting full capacity of available resources. Typically short flying times (10-30 minutes) also limit the possibility of estimating and/or correcting factors such as sensor misalignment and poor attitude initialization of inertial navigation system (INS). This research aims at increasing the accuracy of attitude determination in both absolute and relative senses with no extra sensors onboard. In comparison to classical INS/GNSS setup, novel approach is presented here to integrated state estimation, in which vehicle dynamic model (VDM) is used as the main process model. Such system benefits from available information from autopilot and physical properties of the platform in enhancing performance of determination of trajectory and parameters of exterior orientation consequently. The navigation system employs a differential carrier phase GNSS receiver and a micro electro-mechanical system (MEMS) grade inertial measurement unit (IMU), together with MAV control input from autopilot. Monte-Carlo simulation has been performed on trajectories for typical corridor mapping and block imaging. Results reveal considerable reduction in attitude errors with respect to conventional INS/GNSS system, in both absolute and relative senses. This eventually translates into higher redundancy and accuracy for photogrammetry applications.

This study highlights the benefits of precise aerial position and attitude control in the context of mapping with Micro Aerial Vehicles (MAVs). Accurate mapping with MAVs is gaining importance in applications such as corridor mapping, road and pipeline inspections or mapping of large areas with homogeneous surface structure, e.g. forests or agricultural fields. There, accurate aerial control plays a major role in successful terrain reconstruction and artifact-free ortophoto generation. The presented experiments focus on new approaches of aerial control. We confirm practically that the relative aerial position and attitude control can improve accuracy in difficult mapping scenarios. Indeed, the relative orientation method represents an attractive alternative in the context of MAVs for two reasons. First, the procedure is somewhat simplified, e.g. the angular misalignment, so called boresight, between the camera and the inertial measurement unit (IMU) does not have to be determined and, second, the effect of possible systematic errors in satellite positioning (e.g. due to multipath and/or incorrect recovery of differential carrier-phase ambiguities) is mitigated. First, we present a typical mapping project over an agricultural field and second, we perform a corridor road mapping. We evaluate the proposed methods in scenarios with and without automated image observations. We investigate a recently proposed concept where adjustment is performed using image observations limited to ground control and check points, so called fast aerial triangulation (Fast AT). In this context we show that accurate aerial control (absolute or relative) together with a few image observations can deliver accurate results comparable to classical aerial triangulation with thousands of image measurements. This procedure in turns reduces the demands on processing time and the requirements on the existence of surface texture. Finally, we compare the above mentioned procedures with direct sensor orientation (DiSO) to show its potential for rapid mapping.

This paper presents a novel approach to autonomous navigation for small UAVs, in which the vehicle dynamic model (VDM) serves as the main process model within the navigation filter. The proposed method significantly increases the accuracy and reliability of autonomous navigation, especially for small UAVs with low-cost IMUs on-board. This is achieved with no extra sensor added to the conventional INS/GNSS setup. This improvement is of special interest in case of GNSS outages, where inertial coasting drifts very quickly. In the proposed architecture, the solution to VDM equations provides the estimate of position, velocity, and attitude, which is updated within the navigation filter based on available observations, such as IMU data or GNSS measurements. The VDM is also fed with the control input to the UAV, which is available within the control/autopilot system. The filter is capable of estimating wind velocity and dynamic model parameters, in addition to navigation states and IMU sensor errors. Monte Carlo simulations reveal major improvements in navigation accuracy compared to conventional INS/GNSS navigation system during the autonomous phase, when satellite signals are not available due to physical obstruction or electromagnetic interference for example. In case of GNSS outages of a few minutes, position and attitude accuracy experiences improvements of orders of magnitude compared to inertial coasting. It means that during such scenario, the position-velocity-attitude (PVA) determination is sufficiently accurate to navigate the UAV to a home position without any signal that depends on vehicle environment.

The capability of a previously proposed VDM (vehicle dynamic model) based navigation method for UAVs is assessed in attitude determination without IMU data. This method utilizes the VDM as main process model within the navigation filter and treats data from IMU (if available) and other sensors as observations. Experimental results from a test flight and a Monte Carlo simulation are presented to compare the performance of the VDM-based navigation against the conventional INS-based navigation. While the attitude estimation performance of VDM/GNSS integration is comparable to that of INS/GNSS in simulations, experimental results reveal the need for improved VDM parameter calibration.

The combination of Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS) has become the baseline of many vehicular applications. However, in challenging GNSS scenarios, classical GNSS/INS integration estimators are very sensitive to multiple measurement faults (e.g., due to multipath). In this work, we design a tightly-coupled integration between GNSS and INS where we modify the update step of a classical Extended Kalman Filter (EKF) to consider more robust estimators, like M-estimators. We consider different faulty scenarios where the pseudoranges contain one or several non-modeled biases and ramps. The tightly-coupled GNSS/INS robust Kalman filter performance in the presence of faults is compared with the classical EKF and with a loosely-coupled Robust-GNSS/INS approach. The robust tightly-coupled version is able to minimize more efficiently the faults effect thanks to the direct redundancy of the inertial sensor within the robust estimator.

We present an algorithm for determining the nature of stochastic processes and their parameters based on the analysis of time series of inertial errors. The algorithm is suitable mainly (but not only) for situations where several stochastic processes are superposed. The proposed approach is based on a recently developed method called the Generalized Method of Wavelet Moments (GMWM), whose estimator was proven to be consistent and asymptotically normally distributed. This method delivers a global selection criterion based on the wavelet variance that can be used to determine the suitability of a candidate model (compared to other models) and apply it to low-cost inertial sensors. By allowing candidate model ranking, this approach enables us to construct an algorithm for automatic model identification and determination. The benefits of this methodology are highlighted by providing practical examples of model selection for two types of MEMS IMUs. Copyright (C) 2015 Institute of Navigation.

This paper studies the error behavior of low-cost inertial sensors in dynamic conditions. After proposing a method for error observations per sensor (i.e., gyroscope or accelerometer) and axes, their properties are estimated via the methodology of generalized method of wavelet moments. The developed model parameters are compared with those obtained under static conditions. Then, an attempt is presented to link the parameters of the established model to the dynamic of the vehicle. It is found that a linear relation explains a large portion of the exhibited variability. These findings suggest that the static methods employed for the calibration of inertial sensors could be improved when exploiting such a relationship.

In this study we present a Micro Aerial Vehicle (MAV) equipped with precise position and attitude sensors that together with a pre-calibrated camera enables accurate corridor mapping. The design of the platform is based on widely available model components to which we integrate an open-source autopilot, customized mass-market camera and navigation sensors. We adapt the concepts of system calibration from larger mapping platforms to MAV and evaluate them practically for their achievable accuracy. We present case studies for accurate mapping without ground control points: first for a block configuration, later for a narrow corridor. We evaluate the mapping accuracy with respect to checkpoints and digital terrain model. We show that while it is possible to achieve pixel (3-5 cm) mapping accuracy in both cases, precise aerial position control is sufficient for block configuration, the precise position and attitude control is required for corridor mapping.

This paper presents a development of an open-source flight planning tool for Remotely Piloted Aircraft Systems (RPAS) that is dedicated to high-precision photogrammetric mapping. This tool contains planning functions that are usually available in professional mapping systems for manned aircrafts as well as new features related to GPS signal masking in complex (e.g. mountainous) terrain. The application is based on the open-source Java SDK (Software Development Kit) World Wind from NASA that contains the main geospatial components facilitating the development itself. Besides standard planning functions known from other mission planners, we mainly focus on additional features dealing with safety and accuracy, such as GPS quality assessment. The need for the development came as a response for unifying mission planning across different platforms (e.g. rotary or fixed wing) operating over terrain of different complexity. A special attention is given to the user interface, that is intuitive to use and cost-effective with respect to computer resources.

In this paper, practical realization of a previously proposed VDM based integrated navigation for UAVs is presented. This method utilizes the vehicle dynamic model (VDM) fed by autopilot control commands as main process model within the navigation filter and treats IMU, GNSS or other sensory data as observations. Extensive Monte Carlo simulation results utilizing this approach in previous publications suggested significant improvement in navigation accuracy during long GNSS outages, attitude determination for photogrammetry applications, and robustness in different wind profiles. Preliminary experimental results are presented in this paper on positioning autonomy of an in-house fixed-wing UAV with MEMS IMU onboard during 3 minutes of GNSS outage, revealing more than 20 times improvement in positioning autonomy compared to conventional INS/GNSS integration.

We perform a comparison between an innovation and a residual based integrity monitoring in Extended Kalman Filters for the GNSS/INS hybridization. In this paper, we restrict the study to the detection of abrupt snapshot faults in order to get an intuitive insight. We study the differences in the distribution of the test statistics and the computation of the thresholds. We derive and compare the minimum detectable bias and we provide expressions for the protection levels for both approaches in the single fault situation. We compare them with the classical residual based GNSS RAIM algorithm. Additionally, we perform a sensitivity analysis of the integrity relevant parameters to the inertial sensor quality.