A simplified approach to motion estimation in a UAV using two filters

Schmitz, Gabriel Figueiredo; Alves, Tiago Giacomelli; Henriques, Renato Ventura Bayan; Freitas, Edison P.; Elyoussef, Ebrahim Samer

doi:10.1016/j.ifacol.2016.11.156

Cited by 12 publications

(5 citation statements)

References 14 publications

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“…An MSDF algorithm based on a factor graph is proposed in [24], that can only be used in UAVs for autonomous outdoor flight. In [25], the authors achieved orientation and position estimation by complementary filter and Linear KF, however, this approach, which used GPS and a barometer, can only estimate the position outdoors. In [26], the authors present methods to fuse data from different sensors with a focus on attitude estimation algorithms, which solves the problems of autonomous control, state estimation, path planning, and remote operation, however, this method can only be used indoors.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Onboard 3D State Estimation of an Unmanned Aerial Vehicle in Multi-Environments Using Multi-Sensor Data Fusion

Wang

et al. 2020

Sensors

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The question of how to estimate the state of an unmanned aerial vehicle (UAV) in real time in multi-environments remains a challenge. Although the global navigation satellite system (GNSS) has been widely applied, drones cannot perform position estimation when a GNSS signal is not available or the GNSS is disturbed. In this paper, the problem of state estimation in multi-environments is solved by employing an Extended Kalman Filter (EKF) algorithm to fuse the data from multiple heterogeneous sensors (MHS), including an inertial measurement unit (IMU), a magnetometer, a barometer, a GNSS receiver, an optical flow sensor (OFS), Light Detection and Ranging (LiDAR), and an RGB-D camera. Finally, the robustness and effectiveness of the multi-sensor data fusion system based on the EKF algorithm are verified by field flights in unstructured, indoor, outdoor, and indoor and outdoor transition scenarios.

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Section: Related Workmentioning

confidence: 99%

Real-Time Onboard 3D State Estimation of an Unmanned Aerial Vehicle in Multi-Environments Using Multi-Sensor Data Fusion

Wang

et al. 2020

Sensors

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“…This state estimation system formulation records the sensor measurements and calculates the variance; this is then used as the sensor measurement variance [19]. Examples of this technique are the "ecl" EKF employed by the "Pixhawk 2.1" Multirotor flight controller [20]. This flight controller employs GNSS for positional stabilisation and navigation of multirotor airborne systems [20].…”

Section: Sensor Measurement Variancementioning

confidence: 99%

“…Examples of this technique are the "ecl" EKF employed by the "Pixhawk 2.1" Multirotor flight controller [20]. This flight controller employs GNSS for positional stabilisation and navigation of multirotor airborne systems [20]. In this case, the sensor measurement variance of GNSS is a function of several variables including but not limited to the local RF environment, planetary, and solar weather [21].…”

Section: Sensor Measurement Variancementioning

confidence: 99%

Towards a Model Based Sensor Measurement Variance Input for Extended Kalman Filter State Estimation

Pointon

McLoughlin

Matthews

et al. 2019

Drones

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In this paper, we present an alternate method for the generation and implementation of the sensor measurement variance used in an Extended Kalman Filter (EKF). Furthermore, it demonstrates the limitations of a conventional EKF implementation and postulates an alternate form for representing the sensor measurement variance by extending and improving the characterisation methodology presented in the previous work. As presented in earlier work, the use of surveying grade optical measurement instruments allows for a more effective characterisation of Ultra-Wide Band (UWB) localisation sensors; however, in cluttered environments, the sensor measurement variance will change, making this method not robust. To compensate for the noisier readings, an EKF using a model based sensor measurement variance was developed. This approach allows for a more accurate representation of the sensor measurement variance and leads to a more robust state estimation system. Simulations were run using synthetic data in order to test the effectiveness of the EKF against the originally developed EKF; next, the new EKF was compared to the original EKF using real world data. The new EKF was shown to function much more stably and consistently in less ideal environments for UWB deployment than the previous version.

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“…The proposal was collecting UAV's flight data (e.g., airspeed, ground speed, position, altitude, angle of attack and sideslip) focused on providing accurate and synchronized timestamps to all measurements. Complementary to those studies analyzed above, other studies (see for example [11][12][13][14]) described alternative methods of estimating attitude and position during a UAV's flight performing data fusion algorithms.…”

Section: Related Workmentioning

confidence: 99%

Development and Testing of a Low-Cost Instrumentation Platform for Fixed-Wing UAV Performance Analysis

Silva

Cabreira

Freitas

2018

Drones

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The flight data of a fixed-wing Unmanned Aerial Vehicle (UAV) can be evaluated by its designers in order to analyze its performance, to validate the project criteria and to make new decisions based on the data analyses. In this paper, the authors propose the development of a low-cost instrumentation platform capable of collecting the following data: airspeed, orientation and altitude of the airplane, and the current drained by the electric system. Moreover, this paper presents the use of a telemetry system in order to display the flight conditions to the pilot. The system contains a variety of sensors, which were chosen based on their price, applicability and ease of use. After a test flight had been performed, the collected measurements were plotted and analyzed. Having the flight data, a set of flight characteristics might be observed.

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A simplified approach to motion estimation in a UAV using two filters

Cited by 12 publications

References 14 publications

Real-Time Onboard 3D State Estimation of an Unmanned Aerial Vehicle in Multi-Environments Using Multi-Sensor Data Fusion

Real-Time Onboard 3D State Estimation of an Unmanned Aerial Vehicle in Multi-Environments Using Multi-Sensor Data Fusion

Towards a Model Based Sensor Measurement Variance Input for Extended Kalman Filter State Estimation

Development and Testing of a Low-Cost Instrumentation Platform for Fixed-Wing UAV Performance Analysis

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