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S.U. Pfeiffer
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1
With their ability to access hard-to-reach spaces and to provide a birds-eye view over large areas,Micro Air Vehicles (MAV) are already taking over awide variety of monitoring and inspection tasks. As the continuing miniaturization of electronic components further drives down cost, recent advancements in the design of control algorithms promise a future, where these small drones will be able to operate fully autonomously and with minimal human input. Spatial awareness is a key aspect in developing fully autonomous MAVs and in this dissertation,we develop new algorithms for on-board localization usingUltra-Wideband (UWB) ranging. UWB is a low-power technology that enables data communication and time-of-flight range measurements. The large bandwidth of UWB results in high timing accuracy as well as an improved resistance to shadowing and multipathing, which makes UWB a very useful technology for ranging in indoor environments...
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With their ability to access hard-to-reach spaces and to provide a birds-eye view over large areas,Micro Air Vehicles (MAV) are already taking over awide variety of monitoring and inspection tasks. As the continuing miniaturization of electronic components further drives down cost, recent advancements in the design of control algorithms promise a future, where these small drones will be able to operate fully autonomously and with minimal human input. Spatial awareness is a key aspect in developing fully autonomous MAVs and in this dissertation,we develop new algorithms for on-board localization usingUltra-Wideband (UWB) ranging. UWB is a low-power technology that enables data communication and time-of-flight range measurements. The large bandwidth of UWB results in high timing accuracy as well as an improved resistance to shadowing and multipathing, which makes UWB a very useful technology for ranging in indoor environments...
Conference paper
(2023)
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S.U. Pfeiffer, C. de Wagter, G.C.H.E. de Croon
Moving Horizon Estimation (MHE) offers multiple advantages over Kalman Filters when it comes to the localization of drones. However, due to the high computational cost, they can not be used on Micro Air Vehicles (MAVs) with limited computational power. We have previously shown, that with a few assumptions and simplifications, MHE can be made more efficient while retaining good localization performance. In this paper, we present two additional improvements: the introduction of dynamic step sizes to the gradient descent algorithm, which leads to a significant increase in robustness, and the use of switching variables for outlier rejection, which further reduces the computational load. Both improvements are implemented and assessed in simulation and experiments. Using dynamic step sizes makes it possible to reliably use the estimator on board of a real drone, and the use of Newton’s method specifically opens the option to add different types of measurements. The new outlier rejection method on the other hand is shown to reduce the computational load significantly while having no big impact on accuracy.
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Moving Horizon Estimation (MHE) offers multiple advantages over Kalman Filters when it comes to the localization of drones. However, due to the high computational cost, they can not be used on Micro Air Vehicles (MAVs) with limited computational power. We have previously shown, that with a few assumptions and simplifications, MHE can be made more efficient while retaining good localization performance. In this paper, we present two additional improvements: the introduction of dynamic step sizes to the gradient descent algorithm, which leads to a significant increase in robustness, and the use of switching variables for outlier rejection, which further reduces the computational load. Both improvements are implemented and assessed in simulation and experiments. Using dynamic step sizes makes it possible to reliably use the estimator on board of a real drone, and the use of Newton’s method specifically opens the option to add different types of measurements. The new outlier rejection method on the other hand is shown to reduce the computational load significantly while having no big impact on accuracy.
Journal article
(2022)
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Sven Pfeiffer, Veronica Munaro, Shushuai Li, Alessandro Rizzo, Guido C.H.E. de Croon
Relative localization is a key capability for autonomous robot swarms, and it is a substantial challenge, especially for small flying robots, as they are extremely restricted in terms of sensors and processing while other robots may be located anywhere around them in three-dimensional space. In this article, we generalize wireless ranging-based relative localization to three dimensions. In particular, we show that robots can localize others in three dimensions by ranging to each other and only exchanging body velocities and yaw rates. We perform a nonlinear observability analysis, investigating the observability of relative locations for different cases. Furthermore, we show both in simulation and with real-world experiments that the proposed method can be used for successfully achieving various swarm behaviours. In order to demonstrate the method’s generality, we demonstrate it both on tiny quadrotors and lightweight flapping wing robots.
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Relative localization is a key capability for autonomous robot swarms, and it is a substantial challenge, especially for small flying robots, as they are extremely restricted in terms of sensors and processing while other robots may be located anywhere around them in three-dimensional space. In this article, we generalize wireless ranging-based relative localization to three dimensions. In particular, we show that robots can localize others in three dimensions by ranging to each other and only exchanging body velocities and yaw rates. We perform a nonlinear observability analysis, investigating the observability of relative locations for different cases. Furthermore, we show both in simulation and with real-world experiments that the proposed method can be used for successfully achieving various swarm behaviours. In order to demonstrate the method’s generality, we demonstrate it both on tiny quadrotors and lightweight flapping wing robots.
We present a computationally efficient moving horizon estimator that allows for real-time localization using Ultra-Wideband measurements on small quadrotors. The estimator uses only a single iteration of a simple gradient descent method to optimize the state estimate based on past measurements, while using random sample consensus to reject outliers. We compare our algorithm to a state-of-the-art Extended Kalman Filter and show its advantages when dealing with heavy-tailed noise, which is frequently encountered in Ultra-Wideband ranging. Furthermore, we analyze the algorithm's performance when reducing the number of beacons for measurements and we implement the code on a 30 g Crazyflie drone, to show its ability to run on computationally limited devices.
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We present a computationally efficient moving horizon estimator that allows for real-time localization using Ultra-Wideband measurements on small quadrotors. The estimator uses only a single iteration of a simple gradient descent method to optimize the state estimate based on past measurements, while using random sample consensus to reject outliers. We compare our algorithm to a state-of-the-art Extended Kalman Filter and show its advantages when dealing with heavy-tailed noise, which is frequently encountered in Ultra-Wideband ranging. Furthermore, we analyze the algorithm's performance when reducing the number of beacons for measurements and we implement the code on a 30 g Crazyflie drone, to show its ability to run on computationally limited devices.
Current existing stereo visual odometry algorithms are computationally too expensive for robots with restricted resources. Executing these algorithms on such robots leads to a low frame rate and unacceptable decay in accuracy. We modify S-MSCKF, one of the most computationally efficient stereo Visual Inertial Odometry (VIO) algorithm, to improve its speed and accuracy when tracking low numbers of features. Specifically, we implement the Inverse Lucas-Kanade (ILK) algorithm for feature tracking and stereo matching. An outlier detector based on the average sum square difference of the template and matching warp in the ILK ensures higher robustness, e.g., in the presence of brightness changes. We restrict stereo matching to slide the window only in the x-direction to further decrease the computational costs. Moreover, we limit detection of new features to the regions of interest that have too few features. The modified S-MSCKF uses half of the processing time while obtaining competitive accuracy. This allows the algorithm to run in real-time on the extremely limited Raspberry Pi Zero single-board computer.
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Current existing stereo visual odometry algorithms are computationally too expensive for robots with restricted resources. Executing these algorithms on such robots leads to a low frame rate and unacceptable decay in accuracy. We modify S-MSCKF, one of the most computationally efficient stereo Visual Inertial Odometry (VIO) algorithm, to improve its speed and accuracy when tracking low numbers of features. Specifically, we implement the Inverse Lucas-Kanade (ILK) algorithm for feature tracking and stereo matching. An outlier detector based on the average sum square difference of the template and matching warp in the ILK ensures higher robustness, e.g., in the presence of brightness changes. We restrict stereo matching to slide the window only in the x-direction to further decrease the computational costs. Moreover, we limit detection of new features to the regions of interest that have too few features. The modified S-MSCKF uses half of the processing time while obtaining competitive accuracy. This allows the algorithm to run in real-time on the extremely limited Raspberry Pi Zero single-board computer.