Modulated Motion Blur-based Vehicle Body Velocity and Pose Estimation using an Optical Image Modulator

2021 ◽  
pp. 1-1
Author(s):  
Minyoung Lee ◽  
Jung-Seok Cho ◽  
Kyung-Soo Kim ◽  
Soohyun Kim
Keyword(s):  
Pose Estimation ◽  
Motion Blur ◽  
Optical Image ◽  
Vehicle Body ◽  
Body Velocity

scholarly journals Pose Estimation in Noncontinuous Video Sequences Using Evolutionary Correlation Filtering

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Kenia Picos ◽  
Ulises Orozco-Rosas ◽  
Victor H. Díaz-Ramírez ◽  
Oscar Montiel
Keyword(s):  
Pose Estimation ◽  
Filter Bank ◽  
Motion Blur ◽  
Video Sequences ◽  
Statistical Parameters ◽  
Objective Metrics ◽  
Cluttered Background ◽  
Matching Score ◽  
Correlation Filtering

In this paper, we propose an evolutionary correlation filtering approach for solving pose estimation in noncontinuous video sequences. The proposed algorithm computes the linear correlation between the input scene containing a target in an unknown environment and a bank of matched filters constructed from multiple views of the target and estimates of statistical parameters of the scene. An evolutionary approach for finding the optimal filter that produces the highest matching score in the correlator is implemented. The parameters of the filter bank evolve through generations to refine the quality of pose estimation. The obtained results demonstrate the robustness of the proposed algorithm in challenging image conditions such as noise, cluttered background, abrupt pose changes, and motion blur. The performance of the proposed algorithm yields high accuracy in terms of objective metrics for pose estimation in noncontinuous video sequences.

scholarly journals Vehicle pose estimation algorithm for parking automated guided vehicle

2020 ◽  
Vol 17 (1) ◽  
pp. 172988141989133
Author(s):  
Zhixiong Ning ◽  
Xin Wang ◽  
Jun Wang ◽  
Huafeng Wen
Keyword(s):  
Pose Estimation ◽  
Symmetry Plane ◽  
Point Clouds ◽  
Vehicle Body ◽  
Iterative Closest Point ◽  
Support Vector ◽  
Automated Guided Vehicle ◽  
Alignment Algorithm ◽  
Registration Method ◽  
Iterative Closest Point Algorithm

Parking automated guided vehicle is more and more widely used for efficient automatic parking and one of the tough challenges for parking automated guided vehicle is the problem of vehicle pose estimation. The traditional algorithms rely on the profile information of vehicle body and sensors are required to be mounted at the top of the vehicle. However, the sensors are always mounted at a lower place because the height of a parking automated guided vehicle is always beyond 0.2mm, where we can only get the vehicle wheel information and limited vehicle body information. In this article, a novel method is given based on the symmetry of wheel point clouds collected by 3-D lidar. Firstly, we combine cell-based method with support vector machine classifier to segment ground point clouds. Secondly, wheel point clouds are segmented from obstacle point clouds and their symmetry are corrected by iterative closest point algorithm. Then, we estimate the vehicle pose by the symmetry plane of wheel point clouds. Finally, we compare our method with registration method that combines sample consensus initial alignment algorithm and iterative closest point algorithm. The experiments have been carried out.

A New Computational Procedure to Predict Transient Hydroplaning Performance of a Tire

2001 ◽  
Vol 29 (1) ◽  
pp. 2-22 ◽  
Author(s):  
T. Okano ◽  
M. Koishi
Keyword(s):  
Complex Geometry ◽  
Fluid Flows ◽  
Computational Procedure ◽  
Vehicle Body ◽  
Fluid Viscosity ◽  
Safe Driving ◽  
Transient Phenomena ◽  
Vehicle Velocity ◽  
Fixed Reference ◽  
Volume Method

Abstract “Hydroplaning characteristics” is one of the key functions for safe driving on wet roads. Since hydroplaning depends on vehicle velocity as well as the tire construction and tread pattern, a predictive simulation tool, which reflects all these effects, is required for effective and precise tire development. A numerical analysis procedure predicting the onset of hydroplaning of a tire, including the effect of vehicle velocity, is proposed in this paper. A commercial explicit-type FEM (finite element method)/FVM (finite volume method) package is used to solve the coupled problems of tire deformation and flow of the surrounding fluid. Tire deformations and fluid flows are solved, using FEM and FVM, respectively. To simulate transient phenomena effectively, vehicle-body-fixed reference-frame is used in the analysis. The proposed analysis can accommodate 1) complex geometry of the tread pattern and 2) rotational effect of tires, which are both important functions of hydroplaning simulation, and also 3) velocity dependency. In the present study, water is assumed to be compressible and also a laminar flow, indeed the fluid viscosity, is not included. To verify the effectiveness of the method, predicted hydroplaning velocities for four different simplified tread patterns are compared with experimental results measured at the proving ground. It is concluded that the proposed numerical method is effective for hydroplaning simulation. Numerical examples are also presented in which the present simulation methods are applied to newly developed prototype tires.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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