A terrain segmentation method based on pyramid scene parsing-mobile network for outdoor robots

2021 ◽  
Vol 18 (5) ◽  
pp. 172988142110486
Author(s):  
Botao Zhang ◽  
Tao Hong ◽  
Rong Xiong ◽  
Sergey A Chepinskiy
Keyword(s):  
Real Time ◽  
Feature Fusion ◽  
Robot Navigation ◽  
Computing Time ◽  
Mobile Network ◽  
High Accuracy ◽  
Segmentation Method ◽  
Data Set ◽  
Time Performance ◽  
Scene Parsing

Terrain segmentation is of great significance to robot navigation, cognition, and map building. However, the existing vision-based methods are challenging to meet the high-accuracy and real-time performance. A terrain segmentation method with a novel lightweight pyramid scene parsing mobile network is proposed for terrain segmentation in robot navigation. It combines the feature extraction structure of MobileNet and the encoding path of pyramid scene parsing network. The depthwise separable convolution, the spatial pyramid pooling, and the feature fusion are employed to reduce the onboard computing time of pyramid scene parsing mobile network. A unique data set called Hangzhou Dianzi University Terrain Dataset is constructed for terrain segmentation, which contains more than 4000 images from 10 different scenes. The data set was collected from a robot’s perspective to make it more suitable for robotic applications. Experimental results show that the proposed method has high-accuracy and real-time performance on the onboard computer. Moreover, its real-time performance is better than most state-of-the-art methods for terrain segmentation.

Research on Modeling of Propeller in a Turboprop Engine

2015 ◽  
Vol 32 (2) ◽  
Author(s):  
Jiaqin Huang ◽  
Xianghua Huang ◽  
Tianhong Zhang
Keyword(s):  
Approximate Method ◽  
Real Time ◽  
Integrated Control ◽  
Computing Time ◽  
High Accuracy ◽  
Surface Theory ◽  
Model Based ◽  
Lifting Surface ◽  
Strip Theory ◽  
Lift And Drag

AbstractIn the simulation of engine-propeller integrated control system for a turboprop aircraft, a real-time propeller model with high-accuracy is required. A study is conducted to compare the real-time and precision performance of propeller models based on strip theory and lifting surface theory. The emphasis in modeling by strip theory is focused on three points as follows: First, FLUENT is adopted to calculate the lift and drag coefficients of the propeller. Next, a method to calculate the induced velocity which occurs in the ground rig test is presented. Finally, an approximate method is proposed to obtain the downwash angle of the propeller when the conventional algorithm has no solution. An advanced approximation of the velocities induced by helical horseshoe vortices is applied in the model based on lifting surface theory. This approximate method will reduce computing time and remain good accuracy. Comparison between the two modeling techniques shows that the model based on strip theory which owns more advantage on both real-time and high-accuracy can meet the requirement.

Investigation of the Embedded Control System of Diesel Injector Nozzle Grinding

2014 ◽  
Vol 620 ◽  
pp. 352-357
Author(s):  
Shu Zhen Yang ◽  
Tao Yu ◽  
Yu Jie Bai
Keyword(s):  
Control System ◽  
Real Time ◽  
High Speed ◽  
General Structure ◽  
Control Program ◽  
High Accuracy ◽  
Time Performance ◽  
Injector Nozzle ◽  
Key Factor ◽  
Processing Control

An embebbed system based on S3C6410 controller and Linux operating system is established to overcome the deficiencies of the traditional control system for high accuracy processing of grinding diesel injector nozzle because of its high speed and real-time performance. In this paper,the general structure of the system is designed firstly. Secondly, the detailed hardware platform is constructed and its peripheral equipment is selected. In addition,the software structure is presented. Finally, the main application program,including of processing control program and algorithm of the flow signal processing which is the key factor for high accuracy maching, is studied. The actual processing data shows that this system has good real-time performance, high processing accuracy and stablity.

scholarly journals Object Detection and Classification for Autonomous Drones

2020 ◽  
Vol 8 (6) ◽  
pp. 3162-3165
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
State Of The Art ◽  
High Accuracy ◽  
Multiple Objects ◽  
Time Performance ◽  
Single Frame ◽  
Learning Techniques

Detecting and classifying objects in a single frame which consists of several objects in a cumbersome task. With the advancement of deep learning techniques, the rate of accuracy has increased significantly. This paper aims to implement the state of the art custom algorithm for detection and classification of objects in a single frame with the goal of attaining high accuracy with a real time performance. The proposed system utilizes SSD architecture coupled with MobileNet to achieve maximum accuracy. The system will be fast enough to detect and recognize multiple objects even at 30 FPS.

scholarly journals SD-VIS: A Fast and Accurate Semi-Direct Monocular Visual-Inertial Simultaneous Localization and Mapping (SLAM)

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1511 ◽  
Author(s):  
Quanpan Liu ◽  
Zhengjie Wang ◽  
Huan Wang
Keyword(s):  
Real Time ◽  
Measurement Errors ◽  
Direct Method ◽  
Simultaneous Localization And Mapping ◽  
Sliding Window ◽  
High Accuracy ◽  
Fast Tracking ◽  
Time Performance ◽  
Localization And Mapping ◽  
Feature Based

In practical applications, how to achieve a perfect balance between high accuracy and computational efficiency can be the main challenge for simultaneous localization and mapping (SLAM). To solve this challenge, we propose SD-VIS, a novel fast and accurate semi-direct visual-inertial SLAM framework, which can estimate camera motion and structure of surrounding sparse scenes. In the initialization procedure, we align the pre-integrated IMU measurements and visual images and calibrate out the metric scale, initial velocity, gravity vector, and gyroscope bias by using multiple view geometry (MVG) theory based on the feature-based method. At the front-end, keyframes are tracked by feature-based method and used for back-end optimization and loop closure detection, while non-keyframes are utilized for fast-tracking by direct method. This strategy makes the system not only have the better real-time performance of direct method, but also have high accuracy and loop closing detection ability based on feature-based method. At the back-end, we propose a sliding window-based tightly-coupled optimization framework, which can get more accurate state estimation by minimizing the visual and IMU measurement errors. In order to limit the computational complexity, we adopt the marginalization strategy to fix the number of keyframes in the sliding window. Experimental evaluation on EuRoC dataset demonstrates the feasibility and superior real-time performance of SD-VIS. Compared with state-of-the-art SLAM systems, we can achieve a better balance between accuracy and speed.

scholarly journals Processing chain for 3D histogram of gradients based real-time object recognition

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142097836
Author(s):  
Cristian Vilar ◽  
Silvia Krug ◽  
Benny Thörnberg
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Real Time ◽  
Recognition Accuracy ◽  
3D Object Recognition ◽  
Learning Approaches ◽  
Data Set ◽  
3D Object ◽  
Time Performance ◽  
Voxel Grid

3D object recognition has been a cutting-edge research topic since the popularization of depth cameras. These cameras enhance the perception of the environment and so are particularly suitable for autonomous robot navigation applications. Advanced deep learning approaches for 3D object recognition are based on complex algorithms and demand powerful hardware resources. However, autonomous robots and powered wheelchairs have limited resources, which affects the implementation of these algorithms for real-time performance. We propose to use instead a 3D voxel-based extension of the 2D histogram of oriented gradients (3DVHOG) as a handcrafted object descriptor for 3D object recognition in combination with a pose normalization method for rotational invariance and a supervised object classifier. The experimental goal is to reduce the overall complexity and the system hardware requirements, and thus enable a feasible real-time hardware implementation. This article compares the 3DVHOG object recognition rates with those of other 3D recognition approaches, using the ModelNet10 object data set as a reference. We analyze the recognition accuracy for 3DVHOG using a variety of voxel grid selections, different numbers of neurons ( Nh) in the single hidden layer feedforward neural network, and feature dimensionality reduction using principal component analysis. The experimental results show that the 3DVHOG descriptor achieves a recognition accuracy of 84.91% with a total processing time of 21.4 ms. Despite the lower recognition accuracy, this is close to the current state-of-the-art approaches for deep learning while enabling real-time performance.

Real Time Person Recognition and Attendance Marking Using Holistic Based Approach

2017 ◽  
Vol 7 (7) ◽  
pp. 302
Author(s):  
Reshma P ◽  
Muneer VK ◽  
Muhammed Ilyas P
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
High Accuracy ◽  
Efficient Technique ◽  
Human Face ◽  
Traditional Methods ◽  
Person Recognition ◽  
The Face

Face recognition is a challenging task for the researches. It is very useful for personal verification and recognition and also it is very difficult to implement due to all different situation that a human face can be found. This system makes use of the face recognition approach for the computerized attendance marking of students or employees in the room environment without lectures intervention or the employee. This system is very efficient and requires very less maintenance compared to the traditional methods. Among existing methods PCA is the most efficient technique. In this project Holistic based approach is adapted. The system is implemented using MATLAB and provides high accuracy.

Real-time Performance Monitoring of MPC Based on Covariance Index Prediction

2014 ◽  
Vol 39 (5) ◽  
pp. 658-663 ◽  
Author(s):  
Xue-Min TIAN ◽  
Ya-Jie SHI ◽  
Yu-Ping CAO
Keyword(s):  
Real Time ◽  
Performance Monitoring ◽  
Time Performance
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