State Estimation for Aerial Vehicles in Forest Environments

Chiella, Antonio C. B.; Teixeira, Bruno Otávio Soares; Pereira, Guilherme A. S.

doi:10.1109/icuas.2019.8797822

Cited by 4 publications

(5 citation statements)

References 25 publications

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“…Note that, although we only present a single experiment in this paper, our system was tested before in similar situations. Other results of our localization system working alone can be seen in our previous conference paper [22].…”

Section: Resultsmentioning

confidence: 89%

“…Given these definitions, the modified UKF forecast step is given by ()truexˇ^k|k−1,trueP˜k|k−1xˇxˇ,Pk|k−1xˇtruexˇ¯=UTxfalse^k−1a,Pk−1xaxa,uk−1,f, Pkfalse|k−1truexˇtruexˇ=trueP˜k|k−1xˇxˇ+Q2,k−1, in which, UT(·) is the unscented transform function, as defined in the work by the authors of [22], xk−1a∈…”

Section: Localizationmentioning

confidence: 99%

“…The proposed sensor fusion algorithm can handle the time-varying uncertainty of measurements, such as outliers and slow varying errors. Note that some preliminary results of these contributions were initially presented in our conference paper [22].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

GNSS/LiDAR-Based Navigation of an Aerial Robot in Sparse Forests

Chiella

Machado

Teixeira

et al. 2019

Sensors

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Autonomous navigation of unmanned vehicles in forests is a challenging task. In such environments, due to the canopies of the trees, information from Global Navigation Satellite Systems (GNSS) can be degraded or even unavailable. Also, because of the large number of obstacles, a previous detailed map of the environment is not practical. In this paper, we solve the complete navigation problem of an aerial robot in a sparse forest, where there is enough space for the flight and the GNSS signals can be sporadically detected. For localization, we propose a state estimator that merges information from GNSS, Attitude and Heading Reference Systems (AHRS), and odometry based on Light Detection and Ranging (LiDAR) sensors. In our LiDAR-based odometry solution, the trunks of the trees are used in a feature-based scan matching algorithm to estimate the relative movement of the vehicle. Our method employs a robust adaptive fusion algorithm based on the unscented Kalman filter. For motion control, we adopt a strategy that integrates a vector field, used to impose the main direction of the movement for the robot, with an optimal probabilistic planner, which is responsible for obstacle avoidance. Experiments with a quadrotor equipped with a planar LiDAR in an actual forest environment is used to illustrate the effectiveness of our approach.

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Section: Resultsmentioning

confidence: 89%

Section: Localizationmentioning

confidence: 99%

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GNSS/LiDAR-Based Navigation of an Aerial Robot in Sparse Forests

Chiella

Machado

Teixeira

et al. 2019

Sensors

Self Cite

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show abstract

“…where N is empirically chosen for each sensor to provide some statistical smoothing. Finally, to protect the filter from erratic measurements, outlier rejection using χ 2 measurement gating is used [32,33]. Even when a measurement is rejected, it will be used in the covariance matching to increase the measurement covariance matrix for the sensor so that subsequent measurements can be used.…”

Section: State Estimationmentioning

confidence: 99%

UAS Control under GNSS Degraded and Windy Conditions

Kalaitzakis,

Vitzilaios

2023

Robotics

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Multirotor Uncrewed Aircraft Systems (UAS), widely known as aerial drones, are increasingly used in various indoor and outdoor applications. For outdoor field deployments, the plethora of UAS rely on Global Navigation Satellite Systems (GNSS) for their localization. However, dense environments and large structures can obscure the signal, resulting in a GNSS-degraded environment. Moreover, outdoor operations depend on weather conditions, and UAS flights are significantly affected by strong winds and possibly stronger wind gusts. This work presents a nonlinear model predictive position controller that uses a disturbance observer to adapt to changing weather conditions and fiducial markers to augment the system’s localization. The developed framework can be easily configured for use in multiple different rigid multirotor platforms. The effectiveness of the proposed system is shown through rigorous experimental work in both the lab and the field. The experimental results demonstrate consistent performance, regardless of the environmental conditions and platform used.

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“…Reflections and occlusions in the image usually result in erroneous measurements that may cause the filter to diverge or the controller to fail. To reject any outlying sensor measurement, a χ 2 test is implemented (Chambers et al, 2014;Chiella, Teixeira, & Pereira, 2019;Hausman, Weiss, Brockers, Matthies, & Sukhatme, 2016). Using the innovation of the sensor measurement the χ 2 test is…”

Section: Outlier Rejection and Adaptive Measurement Covariance Matrixmentioning

confidence: 99%

A marsupial robotic system for surveying and inspection of freshwater ecosystems

Kalaitzakis

Cain

Vitzilaios

et al. 2020

Journal of Field Robotics

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Freshwater ecosystems are vast areas that are constantly changing and evolving. To maintain the ecosystem as well as the structures located close to bodies of water, frequent monitoring is required. Although dangerous and time consuming, manual operations are the conventional way of monitoring such areas. Recently, Autonomous Surface Vehicles (ASVs) have been proposed to undertake the monitoring task. As any other platform, ASVs have limitations, such as a restricted point of view and access only where the water is sufficiently deep. Unmanned Aerial Vehicles (UAVs) can fly over any terrain and provide a “bird's‐eye‐view” of the environment. However, UAVs have limited operational time due to power constraints. Heterogeneous marsupial robotic systems use different types of robots to augment their operation envelope, taking advantage of their individual strengths. A marsupial survey system comprised an ASV and a UAV for freshwater monitoring is developed and presented in this paper. This system is able to complete long missions and reach remote locations while also being able to generate detailed maps and inspections of points of interest. The system was thoroughly tested during a 6‐month period in a number of field deployments in freshwater ecosystems at Lake Murray and at the Congaree River, SC, USA, to validate its capabilities.

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State Estimation for Aerial Vehicles in Forest Environments

Cited by 4 publications

References 25 publications

GNSS/LiDAR-Based Navigation of an Aerial Robot in Sparse Forests

GNSS/LiDAR-Based Navigation of an Aerial Robot in Sparse Forests

UAS Control under GNSS Degraded and Windy Conditions

A marsupial robotic system for surveying and inspection of freshwater ecosystems

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