Low-cost, fused millimeter-wave and 3D point cloud imaging for concealed threat detection

2013 ◽  
Author(s):  
Spiros Mantzavinos ◽  
Borja Gonzalez-Valdes ◽  
Dan Busuioc ◽  
Ryan Miller ◽  
Jose A. Martinez-Lorenzo ◽  
...  
Keyword(s):  
Millimeter Wave ◽  
Point Cloud ◽  
Low Cost ◽  
Threat Detection ◽  
3D Point Cloud

scholarly journals Low-Cost Calibration of Matching Error between Lidar and Motor for a Rotating 2D Lidar

2021 ◽  
Vol 11 (3) ◽  
pp. 913
Author(s):  
Chang Yuan ◽  
Shusheng Bi ◽  
Jun Cheng ◽  
Dongsheng Yang ◽  
Wei Wang
Keyword(s):  
Point Cloud ◽  
Low Cost ◽  
Shape Error ◽  
3D Point Cloud ◽  
Motor Shaft ◽  
Matching Error ◽  
Triangular Plate ◽  
Attitude Error ◽  
Shaft Angle ◽  
The Cost

For a rotating 2D lidar, the inaccurate matching between the 2D lidar and the motor is an important error resource of the 3D point cloud, where the error is shown both in shape and attitude. Existing methods need to measure the angle position of the motor shaft in real time to synchronize the 2D lidar data and the motor shaft angle. However, the sensor used for measurement is usually expensive, which can increase the cost. Therefore, we propose a low-cost method to calibrate the matching error between the 2D lidar and the motor, without using an angular sensor. First, the sequence between the motor and the 2D lidar is optimized to eliminate the shape error of the 3D point cloud. Next, we eliminate the attitude error with uncertainty of the 3D point cloud by installing a triangular plate on the prototype. Finally, the Levenberg–Marquardt method is used to calibrate the installation error of the triangular plate. Experiments verified that the accuracy of our method can meet the requirements of the 3D mapping of indoor autonomous mobile robots. While we use a 2D lidar Hokuyo UST-10LX with an accuracy of ±40 mm in our prototype, we can limit the mapping error within ±50 mm when the distance is no more than 2.2996 m for a 1 s scan (mode 1), and we can limit the mapping error within ±50 mm at the measuring range 10 m for a 16 s scan (mode 7). Our method can reduce the cost while the accuracy is ensured, which can make a rotating 2D lidar cheaper.

scholarly journals A Real-Time 3D Perception and Reconstruction System Based on a 2D Laser Scanner

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Zheng Fang ◽  
Shibo Zhao ◽  
Shiguang Wen ◽  
Yu Zhang
Keyword(s):  
Real Time ◽  
Point Cloud ◽  
Low Cost ◽  
Laser Scanner ◽  
Continuous Mapping ◽  
Step Motor ◽  
3D Point Cloud ◽  
3D Perception ◽  
Mapping System ◽  
Local Point

This paper presents a real-time and low-cost 3D perception and reconstruction system which is suitable for autonomous navigation and large-scale environment reconstruction. The 3D mapping system is based on a rotating 2D planar laser scanner driven by a step motor, which is suitable for continuous mapping. However, for such a continuous mapping system, the challenge is that the range measurements are received at different times when the 3D LiDAR is moving, which will result in big distortion of the local 3D point cloud. As a result, the errors in motion estimation can cause misregistration of the resulting point cloud. In order to continuously estimate the trajectory of the sensor, we first extract feature points from the local point cloud and then estimate the transformation between current frame to local map to get the LiDAR odometry. After that, we use the estimated motion to remove the distortion of the local point cloud and then register the undistorted local point cloud to the global point cloud to get accurate global map. Finally, we propose a coarse-to-fine graph optimization method to minimize the global drift. The proposed 3D sensor system is advantageous due to its mechanical simplicity, mobility, low weight, low cost, and real-time estimation. To validate the performance of the proposed system, we carried out several experiments to verify its accuracy, robustness, and efficiency. The experimental results show that our system can accurately estimate the trajectory of the sensor and build a quality 3D point cloud map simultaneously.

scholarly journals POINTO - A LOW COST SOLUTION TO POINT CLOUD PROCESSING

2017 ◽  
Vol XLII-2/W8 ◽  
pp. 111-117 ◽  
Author(s):  
H. Houshiar ◽  
S. Winkler
Keyword(s):  
User Interfaces ◽  
Point Cloud ◽  
Low Cost ◽  
Point Clouds ◽  
3D Point Cloud ◽  
Cloud Data ◽  
Cloud Processing ◽  
3D Point Clouds ◽  
Point Cloud Processing ◽  
Access To Data

With advance in technology access to data especially 3D point cloud data becomes more and more an everyday task. 3D point clouds are usually captured with very expensive tools such as 3D laser scanners or very time consuming methods such as photogrammetry. Most of the available softwares for 3D point cloud processing are designed for experts and specialists in this field and are usually very large software packages containing variety of methods and tools. This results in softwares that are usually very expensive to acquire and also very difficult to use. Difficulty of use is caused by complicated user interfaces that is required to accommodate a large list of features. The aim of these complex softwares is to provide a powerful tool for a specific group of specialist. However they are not necessary required by the majority of the up coming average users of point clouds. In addition to complexity and high costs of these softwares they generally rely on expensive and modern hardware and only compatible with one specific operating system. Many point cloud customers are not point cloud processing experts or willing to spend the high acquisition costs of these expensive softwares and hardwares. In this paper we introduce a solution for low cost point cloud processing. Our approach is designed to accommodate the needs of the average point cloud user. To reduce the cost and complexity of software our approach focuses on one functionality at a time in contrast with most available softwares and tools that aim to solve as many problems as possible at the same time. Our simple and user oriented design improve the user experience and empower us to optimize our methods for creation of an efficient software. In this paper we introduce Pointo family as a series of connected softwares to provide easy to use tools with simple design for different point cloud processing requirements. PointoVIEWER and PointoCAD are introduced as the first components of the Pointo family to provide a fast and efficient visualization with the ability to add annotation and documentation to the point clouds.

scholarly journals THE FEASIBILITY OF 3D POINT CLOUD GENERATION FROM SMARTPHONES

2016 ◽  
Vol XLI-B5 ◽  
pp. 621-626
Author(s):  
N. Alsubaie ◽  
N. El-Sheimy
Keyword(s):  
Point Cloud ◽  
Low Cost ◽  
Bundle Adjustment ◽  
High Signal ◽  
Motion Sensors ◽  
3D Mapping ◽  
3D Point Cloud ◽  
Adjustment Procedure ◽  
Global Matching ◽  
Orientation Parameters

This paper proposes a new technique for increasing the accuracy of direct geo-referenced image-based 3D point cloud generated from low-cost sensors in smartphones. The smartphone’s motion sensors are used to directly acquire the Exterior Orientation Parameters (EOPs) of the captured images. These EOPs, along with the Interior Orientation Parameters (IOPs) of the camera/ phone, are used to reconstruct the image-based 3D point cloud. However, because smartphone motion sensors suffer from poor GPS accuracy, accumulated drift and high signal noise, inaccurate 3D mapping solutions often result. Therefore, horizontal and vertical linear features, visible in each image, are extracted and used as constraints in the bundle adjustment procedure. These constraints correct the relative position and orientation of the 3D mapping solution. Once the enhanced EOPs are estimated, the semi-global matching algorithm (SGM) is used to generate the image-based dense 3D point cloud. Statistical analysis and assessment are implemented herein, in order to demonstrate the feasibility of 3D point cloud generation from the consumer-grade sensors in smartphones.

A portable low-cost 3D point cloud acquiring method based on structure light

2018 ◽  
Author(s):  
Li Gui ◽  
Qiuxia Tang ◽  
Like Zhao ◽  
Xia Huang ◽  
Chao Ge ◽  
...  
Keyword(s):  
Point Cloud ◽  
Low Cost ◽  
3D Point Cloud ◽  
Structure Light

scholarly journals Vineyard Pruning Weight Prediction Using 3D Point Clouds Generated from UAV Imagery and Structure from Motion Photogrammetry

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2489
Author(s):  
Marta García-Fernández ◽  
Enoc Sanz-Ablanedo ◽  
Dimas Pereira-Obaya ◽  
José Ramón Rodríguez-Pérez
Keyword(s):  
Regression Analysis ◽  
Point Cloud ◽  
Low Cost ◽  
Linear Regression Analysis ◽  
Predictor Variable ◽  
Point Clouds ◽  
Aerial Images ◽  
3D Point Cloud ◽  
Area Index ◽  
3D Point Clouds

In viticulture, information about vine vigour is a key input for decision-making in connection with production targets. Pruning weight (PW), a quantitative variable used as indicator of vegetative vigour, is associated with the quantity and quality of the grapes. Interest has been growing in recent years around the use of unmanned aerial vehicles (UAVs) or drones fitted with remote sensing facilities for more efficient crop management and the production of higher quality wine. Current research has shown that grape production, leaf area index, biomass, and other viticulture variables can be estimated by UAV imagery analysis. Although SfM lowers costs, saves time, and reduces the amount and type of resources needed, a review of the literature revealed no studies on its use to determine vineyard pruning weight. The main objective of this study was to predict PW in vineyards from a 3D point cloud generated with RGB images captured by a standard drone and processed by SfM. In this work, vertical and oblique aerial images were taken in two vineyards of Godello and Mencía varieties during the 2019 and 2020 seasons using a conventional Phantom 4 Pro drone. Pruning weight was measured on sampling grids comprising 28 calibration cells for Godello and 59 total cells for Mencía (39 calibration cells and 20 independent validation). The volume of vegetation (V) was estimated from the generated 3D point cloud and PW was estimated by linear regression analysis taking V as predictor variable. When the results were leave-one-out cross-validated (LOOCV), the R2 was found to be 0.71 and the RMSE 224.5 (g) for the PW estimate in Mencía 2020, calculated for the 39 calibration cells on the grounds of oblique images. The regression analysis results for the 20 validation samples taken independently of the rest (R2 = 0.62; RMSE = 249.3 g) confirmed the viability of using the SfM as a fast, non-destructive, low-cost procedure for estimating pruning weight.

scholarly journals UAV-BASED ACQUISITION OF 3D POINT CLOUD – A COMPARISON OF A LOW-COST LASER SCANNER AND SFM-TOOLS

2015 ◽  
Vol XL-3/W3 ◽  
pp. 335-341 ◽  
Author(s):  
D. Mader ◽  
R. Blaskow ◽  
P. Westfeld ◽  
H.-G. Maas
Keyword(s):  
Structure From Motion ◽  
Point Cloud ◽  
Laser Scanning ◽  
Near Infrared ◽  
Low Cost ◽  
Laser Scanner ◽  
Infrared Camera ◽  
Image Data ◽  
3D Point Cloud ◽  
Efficient System

The Project ADFEX (Adaptive Federative 3D Exploration of Multi Robot System) pursues the goal to develop a time- and cost-efficient system for exploration and monitoring task of unknown areas or buildings. A fleet of unmanned aerial vehicles equipped with appropriate sensors (laser scanner, RGB camera, near infrared camera, thermal camera) were designed and built. A typical operational scenario may include the exploration of the object or area of investigation by an UAV equipped with a laser scanning range finder to generate a rough point cloud in real time to provide an overview of the object on a ground station as well as an obstacle map. The data about the object enables the path planning for the robot fleet. Subsequently, the object will be captured by a RGB camera mounted on the second flying robot for the generation of a dense and accurate 3D point cloud by using of structure from motion techniques. In addition, the detailed image data serves as basis for a visual damage detection on the investigated building. <br><br> This paper focuses on our experience with use of a low-cost light-weight Hokuyo laser scanner onboard an UAV. The hardware components for laser scanner based 3D point cloud acquisition are discussed, problems are demonstrated and analyzed, and a quantitative analysis of the accuracy potential is shown as well as in comparison with structure from motion-tools presented.

Sign in / Sign up

Export Citation Format

Share Document