scholarly journals GPS and Stereovision-Based Visual Odometry: Application to Urban Scene Mapping and Intelligent Vehicle Localization

2011 ◽  
Vol 2011 ◽  
pp. 1-17 ◽  
Author(s):  
Lijun Wei ◽  
Cindy Cappelle ◽  
Yassine Ruichek ◽  
Frédérick Zann
Keyword(s):  
Feature Detection ◽  
Reference Frames ◽  
Estimation Method ◽  
Ground Truth ◽  
Urban Environments ◽  
Relative Depth ◽  
Gps Receiver ◽  
Minimization Method ◽  
Vehicle Localization ◽  
Stereoscopic System

We propose an approach for vehicle localization in dense urban environments using a stereoscopic system and a GPS sensor. Stereoscopic system is used to capture the stereo video flow, to recover the environments, and to estimate the vehicle motion based on feature detection, matching, and triangulation from every image pair. A relative depth constraint is applied to eliminate the tracking couples which are inconsistent with the vehicle ego-motion. Then the optimal rotation and translation between the current and the reference frames are computed using an RANSAC based minimization method. Meanwhile, GPS positions are obtained by an on-board GPS receiver and periodically used to adjust the vehicle orientations and positions estimated by stereovision. The proposed method is tested with two real sequences obtained by a GEM vehicle equipped with a stereoscopic system and a RTK-GPS receiver. The results show that the vision/GPS integrated trajectory can fit the ground truth better than the vision-only method, especially for the vehicle orientation. And vice-versa, the stereovision-based motion estimation method can correct the GPS signal failures (e.g., GPS jumps) due to multipath problem or other noises.

Lane Marking-Based Vehicle Localization Using Low-Cost GPS and Open Source Map

2015 ◽  
Vol 03 (04) ◽  
pp. 239-251 ◽  
Author(s):  
Wenjie Lu ◽  
Sergio A. Rodríguez F. ◽  
Emmanuel Seignez ◽  
Roger Reynaud
Keyword(s):  
Low Cost ◽  
Autonomous Vehicle ◽  
Estimation Method ◽  
Dynamic Environment ◽  
Kernel Estimation ◽  
Complex Problem ◽  
Urban Environments ◽  
Vehicle Localization ◽  
Open Map ◽  
High Level

Autonomous Vehicle applications and Advanced Driving Assistance Systems (ADAS) need scene understanding processes, allowing high-level systems to carry out decision. For such systems, the localization of a vehicle evolving in a structured dynamic environment constitutes a complex problem of crucial importance. However, the low accuracy of the global positioning system (GPS) system in urban environments makes its localization unreliable for further treatments. The combination of GPS data and additional sensors (WSS, IMU or Camera) can improve the localization precision. More and more, digital maps are also used in this process. Generally, these maps are customized or built for a specific application, asking high-cost to design and upgrade. In this paper, we propose a low-cost localization system based on camera, GPS and open map. Starting from the road marking, detected by a multi-kernel estimation method, a particle filter generates the samples taking advantage of lane markings to predict the most probable trajectory of the vehicle and the low-cost GPS position. Then, the accuracy of the localization is improved using an open map. This process was validated through several scenarios with a public database and our experimental platform.

scholarly journals Verification of Finger Joint Stiffness Estimation Method With Soft Robotic Actuator

2020 ◽  
Vol 8 ◽  
Author(s):  
Xiang Qian Shi ◽  
Ho Lam Heung ◽  
Zhi Qiang Tang ◽  
Kai Yu Tong ◽  
Zheng Li
Keyword(s):  
Estimation Method ◽  
Joint Stiffness ◽  
Ground Truth ◽  
Clinical Score ◽  
Metacarpophalangeal Joint ◽  
Finger Joint ◽  
Joint Torque ◽  
Hand Rehabilitation ◽  
Stiffness Characteristic ◽  
Modified Ashworth Scale

Stroke has been the leading cause of disability due to the induced spasticity in the upper extremity. The constant flexion of spastic fingers following stroke has not been well described. Accurate measurements for joint stiffness help clinicians have a better access to the level of impairment after stroke. Previously, we conducted a method for quantifying the passive finger joint stiffness based on the pressure-angle relationship between the spastic fingers and the soft-elastic composite actuator (SECA). However, it lacks a ground-truth to demonstrate the compatibility between the SECA-facilitated stiffness estimation and standard joint stiffness quantification procedure. In this study, we compare the passive metacarpophalangeal (MCP) joint stiffness measured using the SECA with the results from our designed standalone mechatronics device, which measures the passive metacarpophalangeal joint torque and angle during passive finger rotation. Results obtained from the fitting model that concludes the stiffness characteristic are further compared with the results obtained from SECA-Finger model, as well as the clinical score of Modified Ashworth Scale (MAS) for grading spasticity. These findings suggest the possibility of passive MCP joint stiffness quantification using the soft robotic actuator during the performance of different tasks in hand rehabilitation.

scholarly journals Robust Sparse Bayesian Learning-Based Off-Grid DOA Estimation Method for Vehicle Localization

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 302 ◽  
Author(s):  
Yun Ling ◽  
Huotao Gao ◽  
Sang Zhou ◽  
Lijuan Yang ◽  
Fangyu Ren
Keyword(s):  
Autonomous Vehicles ◽  
Bayesian Learning ◽  
Rapid Development ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Sparse Bayesian Learning ◽  
Vehicle Localization ◽  
Localization System ◽  
Sparse Signal Representation

With the rapid development of the Internet of Things (IoT), autonomous vehicles have been receiving more and more attention because they own many advantages compared with traditional vehicles. A robust and accurate vehicle localization system is critical to the safety and the efficiency of autonomous vehicles. The global positioning system (GPS) has been widely applied to the vehicle localization systems. However, the accuracy and the reliability of GPS have suffered in some scenarios. In this paper, we present a robust and accurate vehicle localization system consisting of a bistatic passive radar, in which the performance of localization is solely dependent on the accuracy of the proposed off-grid direction of arrival (DOA) estimation algorithm. Under the framework of sparse Bayesian learning (SBL), the source powers and the noise variance are estimated by a fast evidence maximization method, and the off-grid gap is effectively handled by an advanced grid refining strategy. Simulation results show that the proposed method exhibits better performance than the existing sparse signal representation-based algorithms, and performs well in the vehicle localization system.

scholarly journals Voxel-Based Scene Representation for Camera Pose Estimation of a Single RGB Image

2020 ◽  
Vol 10 (24) ◽  
pp. 8866
Author(s):  
Sangyoon Lee ◽  
Hyunki Hong ◽  
Changkyoung Eem
Keyword(s):  
Pose Estimation ◽  
Estimation Method ◽  
Ground Truth ◽  
Training Dataset ◽  
Interest Points ◽  
Camera Pose Estimation ◽  
Polygonal Region ◽  
Camera Pose ◽  
End To End ◽  
Image Pairs

Deep learning has been utilized in end-to-end camera pose estimation. To improve the performance, we introduce a camera pose estimation method based on a 2D-3D matching scheme with two convolutional neural networks (CNNs). The scene is divided into voxels, whose size and number are computed according to the scene volume and the number of 3D points. We extract inlier points from the 3D point set in a voxel using random sample consensus (RANSAC)-based plane fitting to obtain a set of interest points consisting of a major plane. These points are subsequently reprojected onto the image using the ground truth camera pose, following which a polygonal region is identified in each voxel using the convex hull. We designed a training dataset for 2D–3D matching, consisting of inlier 3D points, correspondence across image pairs, and the voxel regions in the image. We trained the hierarchical learning structure with two CNNs on the dataset architecture to detect the voxel regions and obtain the location/description of the interest points. Following successful 2D–3D matching, the camera pose was estimated using n-point pose solver in RANSAC. The experiment results show that our method can estimate the camera pose more precisely than previous end-to-end estimators.

How reliable is statistical wavelet estimation?

Geophysics ◽  
2011 ◽  
Vol 76 (4) ◽  
pp. V59-V68 ◽  
Author(s):  
Jonathan A. Edgar ◽  
Mirko van der Baan
Keyword(s):  
Statistical Method ◽  
Estimation Method ◽  
Ground Truth ◽  
Well Logs ◽  
Deterministic Method ◽  
Wavelet Estimation ◽  
Phase Rotation ◽  
Phase Corrections ◽  
Kurtosis Maximization ◽  
Rate Criterion

Well logs often are used for the estimation of seismic wavelets. The phase is obtained by forcing a well-derived synthetic seismogram to match the seismic, thus assuming the well log provides ground truth. However, well logs are not always available and can predict different phase corrections at nearby locations. Thus, a wavelet-estimation method that reliably can predict phase from the seismic alone is required. Three statistical wavelet-estimation techniques were tested against the deterministic method of seismic-to-well ties. How the choice of method influences the estimated wavelet phase was explored, with the aim of finding a statistical method which consistently predicts a phase in agreement with well logs. It was shown that the statistical method of kurtosis maximization by constant phase rotation consistently is able to extract a phase in agreement with seismic-to-well ties. A statistical method based on a modified mutual-information-rate criterion was demonstrated to provide frequency-dependent phase wavelets where the deterministic method could not. Time-varying statistical wavelets also were estimated with good results — a challenge for deterministic approaches because of the short logging sequence. It was concluded that statistical techniques can be used as quality control tools for the deterministic methods, as a way of extrapolating phase away from wells, or to act as standalone tools in the absence of wells.

CHARACTERIZATION OF FEATURE DETECTION ALGORITHMS FOR A RELIABLE VEHICLE LOCALIZATION

2007 ◽  
Vol 40 (15) ◽  
pp. 149-154
Author(s):  
Cédric Tessier ◽  
Michel Berducat ◽  
Roland Chapuis ◽  
Sébastien Bonnet
Keyword(s):  
Feature Detection ◽  
Vehicle Localization ◽  
Detection Algorithms
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