scholarly journals An Algorithm for Local Dynamic Map Generation for Safe UAV Navigation

Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 88
Author(s):  
Jin-Woo Lee ◽  
Wonjai Lee ◽  
Kyoung-Dae Kim
Keyword(s):  
Operation Time ◽  
Ground Truth ◽  
Velocity Estimation ◽  
Sensor Data ◽  
Local Dynamic ◽  
Memory Usage ◽  
Operation Speed ◽  
Perception System ◽  
Dynamic Map ◽  
Uav Navigation

For safe UAV navigation and to avoid collision, it is essential to have accurate and real-time perception of the environment surrounding the UAV, such as free area detection and recognition of dynamic and static obstacles. The perception system of the UAV needs to recognize information such as the position and velocity of all objects in the surrounding local area regardless of the type of object. At the same time, a probability based representation taking into account the noise of the sensor is also essential. In addition, a software design with efficient memory usage and operation time is required in consideration of the hardware limitations of the UAVs. In this paper, we propose a 3D Local Dynamic Map (LDM) generation algorithm for a perception system for UAVs. The proposed LDM uses a circular buffer as a data structure to ensure low memory usage and fast operation speed. A probability based occupancy map is created using sensor data and the position and velocity of each object are calculated through clustering between grid voxels using the occupancy map and velocity estimation based on a particle filter. The objects are predicted using the position and velocity of each object and this is reflected in the occupancy map. This process is continuously repeated and the flying environment of the UAV can be expressed in a three-dimensional grid map and the state of each object. For the evaluation of the proposed LDM, we constructed simulation environments and the UAV for outdoor flying. As an evaluation factor, the occupancy grid is accuracy evaluated and the ground truth velocity and the estimated velocity are compared.

Evaluation of Commercial Truck Parking Detection for Rest Areas

2018 ◽  
Vol 2672 (9) ◽  
pp. 141-151
Author(s):  
Wei Sun ◽  
Ethan Stoop ◽  
Scott S. Washburn
Keyword(s):  
Software Tool ◽  
Vehicle Detection ◽  
Ground Truth ◽  
Early Morning ◽  
Video Data ◽  
Sensor Data ◽  
Parking Space ◽  
Ground Truth Data ◽  
Detection Technology ◽  
Rest Areas

Florida’s interstate rest areas are heavily utilized by commercial trucks for overnight parking. Many of these rest areas regularly experience 100% utilization of available commercial truck parking spaces during the evening and early-morning hours. Being able to communicate availability of commercial truck parking space to drivers in advance of arriving at a rest area would reduce unnecessary stops at full rest areas as well as driver anxiety. In order to do this, it is critical to implement a vehicle detection technology to reflect the parking status of the rest area correctly. The objective of this project was to evaluate three different wireless in-pavement vehicle detection technologies as applied to commercial truck parking at interstate rest areas. This paper mainly focuses on the following aspects: (a) accuracy of the vehicle detection in parking spaces, (b) installation, setup, and maintenance of the vehicle detection technology, and (c) truck parking trends at the rest area study site. The final project report includes a more detailed summary of the evaluation. The research team recorded video of the rest areas as the ground-truth data and developed a software tool to compare the video data with the parking sensor data. Two accuracy tests (event accuracy and occupancy accuracy) were conducted to evaluate each sensor’s ability to reflect the status of each parking space correctly. Overall, it was found that all three technologies performed well, with accuracy rates of 95% or better for both tests. This result suggests that, for implementation, pricing, and/or maintenance issues may be more significant factors for the choice of technology.

scholarly journals Open-Source Data Collection and Data Sets for Activity Recognition in Smart Homes

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 879 ◽  
Author(s):  
Uwe Köckemann ◽  
Marjan Alirezaie ◽  
Jennifer Renoux ◽  
Nicolas Tsiftes ◽  
Mobyen Uddin Ahmed ◽  
...  
Keyword(s):  
Data Collection ◽  
Activity Recognition ◽  
Care Home ◽  
Open Data ◽  
Ground Truth ◽  
Smart Homes ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Home Setting

As research in smart homes and activity recognition is increasing, it is of ever increasing importance to have benchmarks systems and data upon which researchers can compare methods. While synthetic data can be useful for certain method developments, real data sets that are open and shared are equally as important. This paper presents the E-care@home system, its installation in a real home setting, and a series of data sets that were collected using the E-care@home system. Our first contribution, the E-care@home system, is a collection of software modules for data collection, labeling, and various reasoning tasks such as activity recognition, person counting, and configuration planning. It supports a heterogeneous set of sensors that can be extended easily and connects collected sensor data to higher-level Artificial Intelligence (AI) reasoning modules. Our second contribution is a series of open data sets which can be used to recognize activities of daily living. In addition to these data sets, we describe the technical infrastructure that we have developed to collect the data and the physical environment. Each data set is annotated with ground-truth information, making it relevant for researchers interested in benchmarking different algorithms for activity recognition.

scholarly journals Multi-Hop Dynamic Map Data Propagation Algorithm for Clustered Vehicular Networks

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1728
Author(s):  
Odilbek Urmonov ◽  
HyungWon Kim
Keyword(s):  
Large Scale ◽  
Vehicular Networks ◽  
Dynamic Environment ◽  
Driving Safety ◽  
Local Dynamic ◽  
Vehicular Communication ◽  
Data Propagation ◽  
Dynamic Map ◽  
Map Data ◽  
Transmission Windows

To ensure the driving safety in vehicular network, it is necessary to construct a local dynamic map (LDM) for an extended range. Using the standard vehicular communication protocols, however, vehicles can construct the LDM for only one-hop range. Constructing large-scale LDM is highly challenging because vehicles randomly change their position. This paper proposes a dynamic map propagation (DMP) method, which builds a large aggregated LDM data using a multi-hop communication. To reduce the data overhead, we introduce an efficient clustering method based on a half-circle of the forwarder’s wireless range. The DMP elects one forwarder per cluster, which constructs LDM and forwards it to a neighbor cluster. The inter-cluster interference is minimized by allocating a different transmit window to each cluster. DMP copes with a dynamic environment by frequently re-electing the forwarders and their associated transmission windows. Simulation results reveal that DMP enhances the forwarders’ reception ratio by 20%, while extending LDM dissemination range by 29% over a previous work.

Multiple optimized support vector regression for multi-sensor data fusion of weigh-in-motion system

2020 ◽  
Vol 234 (12) ◽  
pp. 2807-2821
Author(s):  
Xiaofeng Liu ◽  
Zhimin Feng ◽  
Yuehua Chen ◽  
Hongwei Li
Keyword(s):  
Data Fusion ◽  
Support Vector Regression ◽  
Test Sample ◽  
Sensor Data ◽  
Support Vector ◽  
Motion Data ◽  
The Third ◽  
Operation Speed ◽  
Weigh In Motion ◽  
Improved Algorithm

Weigh-in-motion is an efficient way to manage overload vehicles, and usually utilizes multi-sensor to measure vehicle weight at present. To increase generalization and accuracy of support vector regression (SVR) applied in multi-sensor weigh-in-motion data fusion, three improved algorithms are presented in this paper. The first improved algorithm divides train samples into two sets to construct SVR1 and SVR2, respectively, and then test samples are distributed to SVR1 or SVR2 based on the nearest distance principle. The second improved algorithm calculates the theoretical biases of two training samples closeted to one test sample, and then obtains the bias of the test sample by linear interpolation method. The third improved algorithm utilizes the second improved algorithm to realize adaptive adjustment of biases for SVR1 and SVR2. Five vehicles were selected to conduct multi-sensor weigh-in-motion experiments on the built test platform. According to the obtained experiment data, fusion tests of SVR and three improved algorithms are performed, respectively. The results show that three improved algorithms gradually increase accuracy of SVR with fast operation speed, and the third improved algorithm exhibits the best application prospect in multi-sensor weigh-in-motion data fusion.

Wireless Device Based on MEMS Sensors and Bluetooth Low Energy (LE/Smart) Technology for Diagnostics of Mechatronic Systems

2013 ◽  
Vol 460 ◽  
pp. 13-21 ◽  
Author(s):  
Kamil Židek ◽  
Alexander Hošovský
Keyword(s):  
Angular Rate ◽  
Operation Time ◽  
Low Energy ◽  
Sensor Data ◽  
Diagnostic Tools ◽  
Mems Sensors ◽  
Desktop Computer ◽  
Mechatronic Systems ◽  
Communication Interface ◽  
Wireless Device

This paper deals with usability of MEMS sensors for diagnostics of mechatronics system state wirelessly. We can acquire basic kinematics and dynamics mechanism parameters (spatial position, speed, acceleration, vibration, angular rate, orientation, etc.) and some environment condition (local/remote temperature, humidity, pressure, electromagnetic noise) by MEMS sensors. Acquired data are sent to remote application in desktop computer. This system can replace expensive and separate diagnostic tools by small integrated solution with one wireless communication interface (with limitation of MEMS sensors precision). This solution can be battery powered with long operation time, because there is used new wireless technology based on Bluetooth 4 protocol (Low Energy/Smart Bluetooth). Some of integrated MEMS sensors measures same variable on different measuring principle. For example angle can be acquired from three different sensors: magnetometer, accelerometer or gyroscope. Combination of these sensor data can significantly improve value accuracy. The designed diagnostic tool can serve as an inertia measuring unit IMU or Wireless IMU (WIMU).

scholarly journals Application of multispectral sensors and unmanned aerial system in Stara Planina region

2020 ◽  
pp. 175-186
Author(s):  
Nenad Šurjanac ◽  
Marija Milosavljević ◽  
Mara Tabaković-Tošić ◽  
Miroslava Marković
Keyword(s):  
Vegetation Index ◽  
Physiological Activity ◽  
Vegetation Indices ◽  
Ground Truth ◽  
Sensor Data ◽  
Spectral Signature ◽  
Unmanned Aerial System ◽  
Positive Ratio ◽  
Pest Infestation ◽  
Leaf Mining

In the area of Stara Planina mountain, a multispectral survey of forest vegetation was performed. Data acquisition was done with unmanned aerial system DJI Phantom 4 Pro, equipped with integrated RGB 20Mpix sensor, and MicaSense RedEdge M, 5-channel narrowband multispectral sensor. Data was collected in the form of images, and 4 composite orthomosaics were produced-broadband visible RGB, narrowband visible RGB, and with vegetation indices applied NDVI and NDRE. RGB orthomosaic showed no significant changes in canopies apart from the variability of levels of green. Orthomosaics with vegetation indices applied showed changes in the level of physiological activities of leaves. NDVI map showed the negative changes of the top of the canopies, while NDRE map showed more dramatic changes within the canopy as well. Besides the map, 5 polygons with different NDRE values were selected and their respective spectral signature graphs were produced. The areas with the lowest NDRE values had the highest reflectance values in all wavelengths, while the absorption of light is much higher in physiologically active vegetation. Values of NDRE lower than 0.479 were inspected from the ground. This kind of ground-truth provided evidence that the areas coded in red were with lower physiological activity due to the infestation by beech leaf-mining weevil Orchestes fagi L. Another interesting finding was that both NDVI and NDRE values were higher in the areas not directly exposed to the sunlight. The areas shaded by surrounding canopies received only diffuse light but it showed a more positive ratio between absorbed and reflected wavelength which could be characteristic of the Fagus Sylvatica species. The findings in this study showed a strong correlation between low values NDRE vegetation index and negative changes deep within the canopy of high vegetation, which can serve as an indicator of pest infestation in forestry.

scholarly journals A Methodology for Design and Analysis of Sensor Fusion with Real Data in UAV Platforms

2018 ◽  
Author(s):  
Jesús García ◽  
Jose Manuel Molina ◽  
Jorge Trincado
Keyword(s):  
Sensor Fusion ◽  
Performance Indicators ◽  
Performance Metrics ◽  
Ground Truth ◽  
Real Data ◽  
Experimental Methodology ◽  
Sensor Data ◽  
Data Processing Software ◽  
Real Performance ◽  
Best Parameters

This paper presents a methodology to design sensor fusion parameters using real performance indicators of navigation in UAVs based on PixHawk flight controller and peripherals. This methodology and the selected performance indicators allows to find the best parameters for the fusion system of a determined configuration of sensors and a predefined real mission. The selected real platform is described with stress on available sensors and data processing software, and the experimental methodology is proposed to characterize sensor data fusion output and determine the best choice of parameters using quality measurements of tracking output with performance metrics not requiring ground truth.

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