Mixed marker-based/marker-less visual odometry system for mobile robots

When moving in generic indoor environments, robotic platforms generally rely solely on information provided by onboard sensors to determine their position and orientation. However, the lack of absolute references often leads to the introduction of severe drifts in estimates computed, making autonomous operations really hard to accomplish. This paper proposes a solution to alleviate the impact of the above issues by combining two vision‐based pose estimation techniques working on relative and absolute coordinate systems, respectively. In particular, the unknown ground features in the images that are captured by the vertical camera of a mobile platform are processed by a vision‐based odometry algorithm, which is capable of estimating the relative frame‐to‐frame movements. Then, errors accumulated in the above step are corrected using artificial markers displaced at known positions in the environment. The markers are framed from time to time, which allows the robot to maintain the drifts bounded by additionally providing it with the navigation commands needed for autonomous flight. Accuracy and robustness of the designed technique are demonstrated using an off‐the‐shelf quadrotor via extensive experimental test

Architecture and Information Requirements to Assess and Predict Flight Safety Risks During Highly Autonomous Urban Flight Operations

As aviation adopts new and increasingly complex operational paradigms, vehicle types, and technologies to broaden airspace capability and efficiency, maintaining a safe system will require recognition and timely mitigation of new safety issues as they emerge and before significant consequences occur. A shift toward a more predictive risk mitigation capability becomes critical to meet this challenge. In-time safety assurance comprises monitoring, assessment, and mitigation functions that proactively reduce risk in complex operational environments where the interplay of hazards may not be known (and therefore not accounted for) during design. These functions can also help to understand and predict emergent effects caused by the increased use of automation or autonomous functions that may exhibit unexpected non-deterministic behaviors. The envisioned monitoring and assessment functions can look for precursors, anomalies, and trends (PATs) by applying model-based and data-driven methods. Outputs would then drive downstream mitigation(s) if needed to reduce risk. These mitigations may be accomplished using traditional design revision processes or via operational (and sometimes automated) mechanisms. The latter refers to the in-time aspect of the system concept. This report comprises architecture and information requirements and considerations toward enabling such a capability within the domain of low altitude highly autonomous urban flight operations. This domain may span, for example, public-use surveillance missions flown by small unmanned aircraft (e.g., infrastructure inspection, facility management, emergency response, law enforcement, and/or security) to transportation missions flown by larger aircraft that may carry passengers or deliver products. Caveat: Any stated requirements in this report should be considered initial requirements that are intended to drive research and development (R&D). These initial requirements are likely to evolve based on R&D findings, refinement of operational concepts, industry advances, and new industry or regulatory policies or standards related to safety assurance

Position Control of an Unmanned Aerial Vehicle From a Mobile Ground Vehicle

Quadcopters have been developed with controls providing good maneuverability, simple mechanics, and the ability to hover, take-off and land vertically with precision. Due to their small size, they can get close to targets of interest and furthermore stay undetected at lower heights. The main drawbacks of a quadcopter are its high-power consumption and payload restriction, due to which, the number of onboard sensors is constrained. To overcome this limitation, vision-based localization techniques and remote control for the quadcopter are essential areas of current research. The core objective of this research is to develop a closed loop feedback system between an Unmanned Aerial Vehicle (UAV) and a mobile ground vehicle. With this closed loop system, the moving ground vehicle aims to navigate the UAV remotely. The ground vehicle uses a pure pursuit algorithm to traverse a pre-defined path. A Proportional-Integral-Derivative (PID) controller is actualized for position control and attitude stabilization of the UAV. The issue of tracking and 3D pose-estimation of the UAV in light of vision sensing is explored. An estimator to track the states of the UAV, utilizing the images obtained from a single camera mounted on the ground vehicle is developed. This estimator coupled with a Kalman filter determines the UAV\u27s three dimensional position. The relative position of the UAV with the moving ground vehicle and the control output from a joint centralized PD controller is used to navigate the UAV and follow the motion of the ground vehicle in closed loop to avoid time delays. This closed loop system is simulated in MATLAB and Simulink to validate the proposed control and estimation approach. The results obtained validate the control architecture proposed to attain closed loop feedback between the UAV and the mobile ground vehicle

Unmanned Aerial Vehicles (UAVs) in environmental biology: A Review

Acquiring information about the environment is a key step during each study in the field of environmental biology at different levels, from an individual species to community and biome. However, obtaining information about the environment is frequently difficult because of, for example, the phenological timing, spatial distribution of a species or limited accessibility of a particular area for the field survey. Moreover, remote sensing technology, which enables the observation of the Earth’s surface and is currently very common in environmental research, has many limitations such as insufficient spatial, spectral and temporal resolution and a high cost of data acquisition. Since the 1990s, researchers have been exploring the potential of different types of unmanned aerial vehicles (UAVs) for monitoring Earth’s surface. The present study reviews recent scientific literature dealing with the use of UAV in environmental biology. Amongst numerous papers, short communications and conference abstracts, we selected 110 original studies of how UAVs can be used in environmental biology and which organisms can be studied in this manner. Most of these studies concerned the use of UAV to measure the vegetation parameters such as crown height, volume, number of individuals (14 studies) and quantification of the spatio-temporal dynamics of vegetation changes (12 studies). UAVs were also frequently applied to count birds and mammals, especially those living in the water. Generally, the analytical part of the present study was divided into following sections: (1) detecting, assessing and predicting threats on vegetation, (2) measuring the biophysical parameters of vegetation, (3) quantifying the dynamics of changes in plants and habitats and (4) population and behaviour studies of animals. At the end, we also synthesised all the information showing, amongst others, the advances in environmental biology because of UAV application. Considering that 33% of studies found and included in this review were published in 2017 and 2018, it is expected that the number and variety of applications of UAVs in environmental biology will increase in the future

High resolution spatial variability in spring snowmelt for an Arctic shrub-tundra watershed

Arctic tundra environments are characterized by spatially heterogeneous end-of-winter snow cover because of high winds that erode, transport and deposit snow over the winter. This spatially variable end-of-winter snow cover subsequently influences the spatial and temporal variability of snowmelt and results in a patchy snowcover over the melt period. Documenting changes in both snow cover area (SCA) and snow water equivalent (SWE) during the spring melt is essential for understanding hydrological systems, but the lack of high-resolution SCA and SWE datasets that accurately capture micro-scale changes are not commonly available, and do not exist for the Canadian Arctic. This study applies high-resolution remote sensing measurements of SCA and SWE using a fixed-wing Unmanned Aerial System (UAS) to document snowcover changes over the snowmelt period for an Arctic tundra headwater catchment. Repeat measurements of SWE and SCA were obtained for four dominant land cover types (tundra, short shrub, tall shrub, and topographic drift) to provide observations of spatially distributed snowmelt patterns and basin-wide declines in SWE. High-resolution analysis of snowcover conditions over the melt reveal a strong relationship between land cover type, snow distribution, and snow ablation rates whereby shallow snowpacks found in tundra and short shrub regions feature rapid declines in SWE and SCA and became snow-free approximately 10 days earlier than deeper snowpacks. In contrast, tall shrub patches and topographic drift regions were characterized by large initial SWE values and featured a slow decline in SCA. Analysis of basin-wide declines in SCA and SWE reveal three distinct melt phases characterized by 1) low melt rates across a large area resulting in a minor change in SCA, but a very large decline in SWE with, 2) high melt rates resulting in drastic declines in both SCA and SWE, and 3) low melt rates over a small portion of the basin, resulting in little change to either SCA or SWE. The ability to capture high-resolution spatio-temporal changes to tundra snow cover furthers our understanding of the relative importance of various land cover types on the snowmelt timing and amount of runoff available to the hydrological system during the spring freshet