A Spatial Localization and Attitude Estimation System for Unmanned Aerial Vehicles Using a Single Dynamic Vision Sensor

Stuckey, Hunter; Al-Radaideh, Amer; Sun, Liang; Tang, Wei

doi:10.1109/jsen.2022.3187423

Cited by 13 publications

(3 citation statements)

References 38 publications

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“…Many of these drones are of the quadcopter variety [ 71 ]. While most drones require remote piloting, there has been an increasing element of automation to their navigation [ 72 , 73 ], odometry, landing, and trajectory systems. AI models trained for these purposes use pathways, object images, and balance data models.…”

Section: Applicationsmentioning

confidence: 99%

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

Biglari

Tang

2023

Sensors

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Machine learning is an expanding field with an ever-increasing role in everyday life, with its utility in the industrial, agricultural, and medical sectors being undeniable. Recently, this utility has come in the form of machine learning implementation on embedded system devices. While there have been steady advances in the performance, memory, and power consumption of embedded devices, most machine learning algorithms still have a very high power consumption and computational demand, making the implementation of embedded machine learning somewhat difficult. However, different devices can be implemented for different applications based on their overall processing power and performance. This paper presents an overview of several different implementations of machine learning on embedded systems divided by their specific device, application, specific machine learning algorithm, and sensors. We will mainly focus on NVIDIA Jetson and Raspberry Pi devices with a few different less utilized embedded computers, as well as which of these devices were more commonly used for specific applications in different fields. We will also briefly analyze the specific ML models most commonly implemented on the devices and the specific sensors that were used to gather input from the field. All of the papers included in this review were selected using Google Scholar and published papers in the IEEExplore database. The selection criterion for these papers was the usage of embedded computing systems in either a theoretical study or practical implementation of machine learning models. The papers needed to have provided either one or, preferably, all of the following results in their studies—the overall accuracy of the models on the system, the overall power consumption of the embedded machine learning system, and the inference time of their models on the embedded system. Embedded machine learning is experiencing an explosion in both scale and scope, both due to advances in system performance and machine learning models, as well as greater affordability and accessibility of both. Improvements are noted in quality, power usage, and effectiveness.

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Section: Applicationsmentioning

confidence: 99%

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

Biglari

Tang

2023

Sensors

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“…The change-reflecting method will inevitably lead to information loss for the static scene. Stuckey et al 26 proposed a temporal video filter to process DVS stream data and achieved three-dimensional localization for UAVs. Zhang et al 27 designed a spike encoding module and a spiking neural network for object tracking in DVS.…”

Section: Introductionmentioning

confidence: 99%

Trajectory tracking method based on Bayesian classifier for pulse array image sensor

Zhang

Gao

et al. 2023

Opt. Eng.

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.A pulse array image sensor (PAIS) is a bionic image sensor that converts light intensity into a pulse sequence to reduce the data volume. The traditional tracking method is not suitable for the sparse data form because of the absence of grayscale information. A trajectory tracking method based on a Bayesian classifier is proposed to maximize the property of the pulse data. First, candidate points with the smallest interval distances are selected in the area of interest. Then the total pulse numbers in a specified period at different positions are gathered to compose the positive and negative feature vectors, which are used to train a naive Bayesian classifier. The classifier can exactly obtain positions from the candidate points, and features in the new tracking positions train and update the classifier. The two-step filtering target point tracking algorithm using the interval distance and Bayesian classifier requires only the raw pulse data without processing, which can maximize the advantages of the special data format and improve computing efficiency. In this way, the position information can be directly obtained from pulse data, and the problem of wasting computing resources on reconstructing grayscale images and treating them in the traditional way is avoided. Experiments were performed on both real filmed data and the public event camera datasets. Our method can obtain trajectories with a high accuracy and long tracking time. The tracking errors are in single digits. In the comparison experiments, although our method has a smaller data volume than other models that obtain both frame and event data, the results still show that it has a comparable performance to the state-of-the-art methods.

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“…However, they are also constrained by factors such as accuracy, cost, weight, and environmental limitations. Vision sensors, due to their excellent performance in these areas, therefore have become a popular choice for obtaining motion information in the system [8].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Optimal States Estimation with Inertial and Delayed Visual Measurements for Unmanned Aerial Vehicles

Sun,

Zhang,

Zou

et al. 2023

Sensors

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Motion estimation is a major issue in applications of Unmanned Aerial Vehicles (UAVs). This paper proposes an entire solution to solve this issue using information from an Inertial Measurement Unit (IMU) and a monocular camera. The solution includes two steps: visual location and multisensory data fusion. In this paper, attitude information provided by the IMU is used as parameters in Kalman equations, which are different from pure visual location methods. Then, the location of the system is obtained, and it will be utilized as the observation in data fusion. Considering the multiple updating frequencies of sensors and the delay of visual observation, a multi-rate delay-compensated optimal estimator based on the Kalman filter is presented, which could fuse the information and obtain the estimation of 3D positions as well as translational speed. Additionally, the estimator was modified to minimize the computational burden, so that it could run onboard in real time. The performance of the overall solution was assessed using field experiments on a quadrotor system, compared with the estimation results of some other methods as well as the ground truth data. The results illustrate the effectiveness of the proposed method.

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A Spatial Localization and Attitude Estimation System for Unmanned Aerial Vehicles Using a Single Dynamic Vision Sensor

Cited by 13 publications

References 38 publications

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

Trajectory tracking method based on Bayesian classifier for pulse array image sensor

Real-Time Optimal States Estimation with Inertial and Delayed Visual Measurements for Unmanned Aerial Vehicles

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