scholarly journals An Analytical Measuring Rectification Algorithm of Monocular Systems in Dynamic Environment

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Deshi Li ◽  
Xiaoliang Wang
Keyword(s):  
Autonomous Vehicles ◽  
Vision System ◽  
Dynamic Environment ◽  
Outdoor Environment ◽  
Safe Distance ◽  
Range Estimation ◽  
Vision Navigation ◽  
Nonholonomic Dynamics ◽  
Yaw Angle ◽  
Range Variation

Range estimation is crucial for maintaining a safe distance, in particular for vision navigation and localization. Monocular autonomous vehicles are appropriate for outdoor environment due to their mobility and operability. However, accurate range estimation using vision system is challenging because of the nonholonomic dynamics and susceptibility of vehicles. In this paper, a measuring rectification algorithm for range estimation under shaking conditions is designed. The proposed method focuses on how to estimate range using monocular vision when a shake occurs and the algorithm only requires the pose variations of the camera to be acquired. Simultaneously, it solves the problem of how to assimilate results from different kinds of sensors. To eliminate measuring errors by shakes, we establish a pose-range variation model. Afterwards, the algebraic relation between distance increment and a camera’s poses variation is formulated. The pose variations are presented in the form of roll, pitch, and yaw angle changes to evaluate the pixel coordinate incensement. To demonstrate the superiority of our proposed algorithm, the approach is validated in a laboratory environment using Pioneer 3-DX robots. The experimental results demonstrate that the proposed approach improves in the range accuracy significantly.

scholarly journals LVD-NMPC: A learning-based vision dynamics approach to nonlinear model predictive control for autonomous vehicles

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110195
Author(s):  
Sorin Grigorescu ◽  
Cosmin Ginerica ◽  
Mihai Zaha ◽  
Gigel Macesanu ◽  
Bogdan Trasnea
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Autonomous Vehicles ◽  
Process Model ◽  
Network Performance ◽  
Vision System ◽  
Nonlinear Model Predictive Control ◽  
Scaled Model

In this article, we introduce a learning-based vision dynamics approach to nonlinear model predictive control (NMPC) for autonomous vehicles, coined learning-based vision dynamics (LVD) NMPC. LVD-NMPC uses an a-priori process model and a learned vision dynamics model used to calculate the dynamics of the driving scene, the controlled system’s desired state trajectory, and the weighting gains of the quadratic cost function optimized by a constrained predictive controller. The vision system is defined as a deep neural network designed to estimate the dynamics of the image scene. The input is based on historic sequences of sensory observations and vehicle states, integrated by an augmented memory component. Deep Q-learning is used to train the deep network, which once trained can also be used to calculate the desired trajectory of the vehicle. We evaluate LVD-NMPC against a baseline dynamic window approach (DWA) path planning executed using standard NMPC and against the PilotNet neural network. Performance is measured in our simulation environment GridSim, on a real-world 1:8 scaled model car as well as on a real size autonomous test vehicle and the nuScenes computer vision dataset.

Vision Navigation with Salient Features for Autonomous Aircraft

2013 ◽  
Vol 278-280 ◽  
pp. 1237-1241
Author(s):  
Jun Wei Yu ◽  
Nan Liu ◽  
Gui Cai Wang ◽  
Xiao Bo Jin
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Unscented Kalman Filter ◽  
Vision System ◽  
Control Points ◽  
Vision Navigation ◽  
State Information ◽  
Aircraft Navigation ◽  
Autonomous Aircraft ◽  
Sequential Images

A novel technique of vision-aided navigation for autonomous aircraft is presented in this paper. The aircraft’s position and pose are estimated with several control points. The saliency descriptor of corner is defined and the control points are selected according their saliency. Control points are tracked in sequential images based on Fourier-Melline transform. The unscented Kalman filter is used to fuse the aircraft state information provided by the vision system and the inertial navigation system. Experiments show that the accuracy, efficiency and robustness of aircraft navigation system are improved with the proposed method.

An Road Obstacle Detection Method Based on Monocular Measurement

2011 ◽  
Vol 55-57 ◽  
pp. 539-544
Author(s):  
Hong Jiao Jin ◽  
Shen Lin ◽  
Shi Guang Luo
Keyword(s):  
Detection Method ◽  
Vision System ◽  
Measurement Model ◽  
Obstacle Detection ◽  
Monocular Vision ◽  
Projection Model ◽  
Vision Navigation ◽  
Acceptable Range ◽  
World Coordinate System ◽  
The World

Obstacle detection in the intelligent vehicle vision navigation system occupies a very important role. The studies for the obstacles detecting, especially Monocular Measurement from the computer vision, simplifying monocular vision system to camera projection model. Getting the conversion relation between image coordinate and the world coordinate system through the geometry derivation to establish the measurement model and achieve the obstacle measurement. The experiment proved that the error of this measurement model selected is within the acceptable range.

scholarly journals Adaptive Safe Distance Prediction Using MPC for Bridge Cranes Considering Anti-Swing in Dynamic Environment

2020 ◽  
Author(s):  
Huili Chen ◽  
Guoliang Liu ◽  
Guohui Tian ◽  
Jianhua Zhang ◽  
Ze Ji
Keyword(s):  
Dynamic Environment ◽  
Prediction Method ◽  
Bridge Crane ◽  
Safe Distance ◽  
Moving Obstacles ◽  
Dangerous Situation ◽  
The Mean ◽  
Prediction Control ◽  
Distance Prediction ◽  
Mean Time

<div>In dynamic environment, the suddenly appeared </div><div>human or other moving obstacles can affect the safety of the </div><div>bridge crane. For such dangerous situation, the bridge crane </div><div>must predict potential collisions between the payload and the </div><div>obstacle, keep safe distance while the swing of the payload must </div><div>be considered in the mean time. Therefore, the safe distance is </div><div>not a constant value, which must be adaptive to the relative </div><div>speed of the bridge crane. However, as far as we know, the </div><div>mathematical model between the safe distance and the relative </div><div>speed of the bridge crane has never been fully discussed. In </div><div>this paper, we propose a safe distance prediction method using </div><div>model prediction control (MPC), which can make sure that the </div><div>crane can stop before the obstacle, and avoid possible collisions, </div><div>while the relative speed and anti-swing are both considered. The </div><div>experimental results prove the effectiveness of our idea.</div>

scholarly journals A Seamless Navigation System and Applications for Autonomous Vehicles Using a Tightly Coupled GNSS/UWB/INS/Map Integration Scheme

2021 ◽  
Vol 14 (1) ◽  
pp. 27
Author(s):  
Changqiang Wang ◽  
Aigong Xu ◽  
Xin Sui ◽  
Yushi Hao ◽  
Zhengxu Shi ◽  
...  
Keyword(s):  
High Precision ◽  
Navigation System ◽  
Autonomous Vehicles ◽  
Outdoor Environment ◽  
Precision Positioning ◽  
Outdoor Environments ◽  
Tightly Coupled ◽  
Seamless Positioning ◽  
Indoor And Outdoor Environments ◽  
Indoor And Outdoor

Seamless positioning systems for complex environments have been a popular focus of research on positioning safety for autonomous vehicles (AVs). In particular, the seamless high-precision positioning of AVs indoors and outdoors still poses considerable challenges and requires continuous, reliable, and high-precision positioning information to guarantee the safety of driving. To obtain effective positioning information, multiconstellation global navigation satellite system (multi-GNSS) real-time kinematics (RTK) and an inertial navigation system (INS) have been widely integrated into AVs. However, integrated multi-GNSS and INS applications cannot provide effective and seamless positioning results for AVs in indoor and outdoor environments due to limited satellite availability, multipath effects, frequent signal blockages, and the lack of GNSS signals indoors. In this contribution, multi-GNSS-tightly coupled (TC) RTK/INS technology is developed to solve the positioning problem for a challenging urban outdoor environment. In addition, ultrawideband (UWB)/INS technology is developed to provide accurate and continuous positioning results in indoor environments, and INS and map information are used to identify and eliminate UWB non-line-of-sight (NLOS) errors. Finally, an improved adaptive robust extended Kalman filter (AREKF) algorithm based on a TC integrated single-frequency multi-GNSS-TC RTK/UWB/INS/map system is studied to provide continuous, reliable, high-precision positioning information to AVs in indoor and outdoor environments. Experimental results show that the proposed scheme is capable of seamlessly guaranteeing the positioning accuracy of AVs in complex indoor and outdoor environments involving many measurement outliers and environmental interference effects.

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