Triad system for object's 3D localization using low-resolution 2D ultrasonic sensor array

AbstractIn the recently published researches in the object localization field, 3D object localization takes the largest part of this research due to its importance in our daily life. 3D object localization has many applications such as collision avoidance, robotic guiding and vision and object surfaces topography modeling. This research study represents a novel localization algorithm and system design using a low-resolution 2D ultrasonic sensor array for 3D real-time object localization. A novel localization algorithm is developed and applied to the acquired data using the three sensors having the minimum calculated distances at each acquired sample, the algorithm was tested on objects at different locations in 3D space and validated with acceptable level of precision and accuracy. Polytope Faces Pursuit (PFP) algorithm was used for finding an approximate sparse solution to the object location from the measured three minimum distances. The proposed system successfully localizes the object at different positions with an error average of ±1.4 mm, ±1.8 mm, and ±3.7 mm in x-direction, y-direction, and z-direction, respectively, which are considered as low error rates.

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MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33018851 ◽

2019 ◽

Vol 33 ◽

pp. 8851-8858 ◽

Cited By ~ 25

Author(s):

Zengyi Qin ◽

Jinglu Wang ◽

Yan Lu

Keyword(s):

Depth Estimation ◽

Geometric Reasoning ◽

Object Localization ◽

3D Object ◽

Global Context ◽

3D Space ◽

3D Localization ◽

Depth Dimension ◽

Bounding Boxes ◽

Rgb Image

Localizing objects in the real 3D space, which plays a crucial role in scene understanding, is particularly challenging given only a single RGB image due to the geometric information loss during imagery projection. We propose MonoGRNet for the amodal 3D object localization from a monocular RGB image via geometric reasoning in both the observed 2D projection and the unobserved depth dimension. MonoGRNet is a single, unified network composed of four task-specific subnetworks, responsible for 2D object detection, instance depth estimation (IDE), 3D localization and local corner regression. Unlike the pixel-level depth estimation that needs per-pixel annotations, we propose a novel IDE method that directly predicts the depth of the targeting 3D bounding box’s center using sparse supervision. The 3D localization is further achieved by estimating the position in the horizontal and vertical dimensions. Finally, MonoGRNet is jointly learned by optimizing the locations and poses of the 3D bounding boxes in the global context. We demonstrate that MonoGRNet achieves state-of-the-art performance on challenging datasets.

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Superquadrics Model-based 3D Object Localization Algorithm

ROBOT ◽

10.3724/sp.j.1218.2013.00439 ◽

2013 ◽

Vol 35 (4) ◽

pp. 439 ◽

Cited By ~ 2

Author(s):

Lin WANG ◽

Jianfu CAO ◽

Chongzhao HAN

Keyword(s):

Object Localization ◽

Localization Algorithm ◽

3D Object ◽

Model Based

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Three-Dimensional Wind Measurement Based on Ultrasonic Sensor Array and Multiple Signal Classification

Sensors ◽

10.3390/s20020523 ◽

2020 ◽

Vol 20 (2) ◽

pp. 523 ◽

Cited By ~ 3

Author(s):

Bian Ma ◽

Jing Teng ◽

Huixian Zhu ◽

Rong Zhou ◽

Yun Ju ◽

...

Keyword(s):

Wind Speed ◽

Wind Turbine ◽

Wind Power ◽

Sensor Array ◽

Three Dimensional ◽

Ultrasonic Sensor ◽

Signal Classification ◽

Multiple Signal Classification ◽

Wind Turbine Control ◽

3D Space

The wind power industry continues to experience rapid growth worldwide. However, the fluctuations in wind speed and direction complicate the wind turbine control process and hinder the integration of wind power into the electrical grid. To maximize wind utilization, we propose to precisely measure the wind in a three-dimensional (3D) space, thus facilitating the process of wind turbine control. Natural wind is regarded as a 3D vector, whose direction and magnitude correspond to the wind’s direction and speed. A semi-conical ultrasonic sensor array is proposed to simultaneously measure the wind speed and direction in a 3D space. As the ultrasonic signal transmitted between the sensors is influenced by the wind and environment noise, a Multiple Signal Classification algorithm is adopted to estimate the wind information from the received signal. The estimate’s accuracy is evaluated in terms of root mean square error and mean absolute error. The robustness of the proposed method is evaluated by the type A evaluation of standard uncertainty under a varying signal-to-noise ratio. Simulation results validate the accuracy and anti-noise performance of the proposed method, whose estimated wind speed and direction errors converge to zero when the SNR is over 15 dB.

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3-Dimensional Localization System Based on Extension of Beacon Nodes and Segmentation of Coordinate Space

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.988.489 ◽

2014 ◽

Vol 988 ◽

pp. 489-497 ◽

Cited By ~ 1

Author(s):

Dong Myung Lee ◽

Ho Chul Lee ◽

Yun Hae Kim

Keyword(s):

Mobile Node ◽

Coordinate Space ◽

Localization Algorithm ◽

Localization System ◽

3 Dimensional ◽

3D Space ◽

Performance Metric ◽

3D Localization ◽

System 2 ◽

Beacon Node

In this paper, the 3 Dimensional (3D) localization system based on extension of beacon nodes and segmentation of coordinate space is proposed and the performance of the system is analyzed. The main functions in the 3D localization system are 1) the equations for calculating the location coordinates of the 3D localization system by intersection of 3 sphere equations; 2) the extension concept of beacon nodes; 3) the compensation scheme for the 3D localization algorithm; 4) calculation of the location coordinate in selected segmented space. The distance error (Derror) between the beacon and mobile node can be derived and it is used to performance metric for proposed system. It can be inferred that 1) LSCA is more stable and reliable system than LSNOCA, and it can be more useful to the practical localization system; 2) The Derror is the smallest value when the segmented distance of 3D space for the 3D localization system is set to be 1m; 3) The Derror when the height of the beacon node is 1.5m is largely lower than when that is 2.3m.

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Improving Object Localization Resolution using a Wearable Ultrasonic Sensor Array

2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS) ◽

10.1109/mwscas48704.2020.9184439 ◽

2020 ◽

Author(s):

Sylmarie Davila-Montero ◽

Sina Parsnejad ◽

Yousef Gtat ◽

Andrew J. Mason

Keyword(s):

Sensor Array ◽

Ultrasonic Sensor ◽

Object Localization

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Action Prediction Using Extremely Low-Resolution Thermopile Sensor Array For Elderly Monitoring

10.1109/icip42928.2021.9506024 ◽

2021 ◽

Author(s):

Igor Morawski ◽

Wen-Nung Lie ◽

Jui-Chiu Chiang

Keyword(s):

Sensor Array ◽

Action Prediction ◽

Low Resolution ◽

Thermopile Sensor

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Estimation for Direction of Signal Arrival based on Area Density of Estimated Lines for Ultrasonic Sensor Array

2020 IEEE 9th Global Conference on Consumer Electronics (GCCE) ◽

10.1109/gcce50665.2020.9291797 ◽

2020 ◽

Author(s):

Eichi MORITA ◽

Hiroyuki HATANO ◽

Kosuke SANADA ◽

Kazuo MORI ◽

Asuka TSUJII ◽

...

Keyword(s):

Sensor Array ◽

Ultrasonic Sensor ◽

Area Density ◽

Signal Arrival

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Video Nodes Based Collaborative Object Localization Framework in Wireless Multimedia Sensor Networks

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.317-319.1078 ◽

2011 ◽

Vol 317-319 ◽

pp. 1078-1083 ◽

Cited By ~ 1

Author(s):

Qing Tao Lin ◽

Xiang Bing Zeng ◽

Xiao Feng Jiang ◽

Xin Yu Jin

Keyword(s):

Cluster Head ◽

Computation Time ◽

Least Square Method ◽

Least Square ◽

Object Localization ◽

Wireless Multimedia ◽

Localization Algorithm ◽

Localization Accuracy ◽

Multimedia Sensor Networks ◽

Localization Model

This paper establishes a 3-D localization model and based on this model, it proposes a collaborative localization framework. In this framework, node that observes the object sends its attitude information and the relative position of the object's projection in its camera to the cluster head. The cluster head adopts an algorithm proposed in this paper to select some nodes to participate localization. The localization algorithm is based on least square method. Because the localization framework is based on a 3-D model, the size of the object or other prerequisites is not necessary. At the end of this paper, a simulation is taken on the numbers of nodes selected to locate and the localization accuracy. The result implies that selecting 3~4 nodes is proper. The theoretical analysis and the simulation result also imply that a const computation time cost is paid in this framework with a high localization accuracy (in our simulation environment, a 0.01 meter error).

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