Three-Dimensional Occupancy Grids With the Use of Vision and Proximity Sensors in a Robotic Workcell

2004 ◽  
Author(s):  
Manish Kumar ◽  
Devendra P. Garg
Keyword(s):  
Neural Network ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Positional Information ◽  
Feedforward Neural Network ◽  
Robot Calibration ◽  
Proximity Sensor ◽  
Vision Sensors ◽  
Robot Wrist ◽  
Occupancy Grids

This paper discusses the use of multiple vision sensors and a proximity sensor to obtain three-dimensional occupancy profile of robotic workspace, identify key features, and obtain a 3-D model of the objects in the work space. The present research makes use of three identical vision sensors. Two of these sensors are mounted on a stereo rig on the sidewall of the robotic workcell. The third vision sensor is located above the workcell. The vision sensors on the stereo rig provide information about three-dimensional position of any point in the robotic workspace. The camera to robot calibration for these vision sensors in stereo configuration has been obtained with the help of a three-layered feedforward neural network. Squared Sum of Difference (SSD) algorithm has been used to obtain the stereo matching. Similarly, camera to robot transformation for the camera located above the work cell has been obtained with the help of a three-layered feedforward neural network. Three-dimensional positional information from vision sensors on stereo rig and two-dimensional positional information from a camera located above the workcell and a proximity sensor mounted on the robot wrist have been fused with the help of Bayesian technique to obtain more accurate positional information about locations in workspace.

scholarly journals Stereo Matching Based on Immune Neural Network in Abdomen Reconstruction

2015 ◽  
Vol 2015 ◽  
pp. 1-15
Author(s):  
Huan Liu ◽  
Kuangrong Hao ◽  
Yongsheng Ding ◽  
Chunjuan Ouyang
Keyword(s):  
Neural Network ◽  
Stereo Matching ◽  
Feature Matching ◽  
Three Dimensional ◽  
Target Point ◽  
Feature Points ◽  
Matching Algorithm ◽  
Optimization Task ◽  
Feasible Alternative ◽  
Single Neural Network

Stereo feature matching is a technique that finds an optimal match in two images from the same entity in the three-dimensional world. The stereo correspondence problem is formulated as an optimization task where an energy function, which represents the constraints on the solution, is to be minimized. A novel intelligent biological network (Bio-Net), which involves the human B-T cells immune system into neural network, is proposed in this study in order to learn the robust relationship between the input feature points and the output matched points. A model from input-output data (left reference point-right target point) is established. In the experiments, the abdomen reconstructions for different-shape mannequins are then performed by means of the proposed method. The final results are compared and analyzed, which demonstrate that the proposed approach greatly outperforms the single neural network and the conventional matching algorithm in precise. Particularly, as far as time cost and efficiency, the proposed method exhibits its significant promising and potential for improvement. Hence, it is entirely considered as an effective and feasible alternative option for stereo matching.

Stereo Matching Algorithm Based on Three-Dimensional Convolutional Neural Network

2019 ◽  
Vol 39 (11) ◽  
pp. 1115001
Author(s):  
王玉锋 Wang Yufeng ◽  
王宏伟 Wang Hongwei ◽  
于光 Yu Guang ◽  
杨明权 Yang Mingquan ◽  
袁昱纬 Yuan Yuwei ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Matching Algorithm

scholarly journals A new function of stereo matching algorithm based on hybrid convolutional neural network

2022 ◽  
Vol 25 (1) ◽  
pp. 223
Author(s):  
Mohd Saad Hamid ◽  
Nurulfajar Abd Manap ◽  
Rostam Affendi Hamzah ◽  
Ahmad Fauzan Kadmin ◽  
Shamsul Fakhar Abd Gani ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Bilateral Filter ◽  
Disparity Map ◽  
Matching Algorithm ◽  
Matching Cost ◽  
The Difference ◽  
Aggregation Step

This paper proposes a new hybrid method between the learning-based and handcrafted methods for a stereo matching algorithm. The main purpose of the stereo matching algorithm is to produce a disparity map. This map is essential for many applications, including three-dimensional (3D) reconstruction. The raw disparity map computed by a convolutional neural network (CNN) is still prone to errors in the low texture region. The algorithm is set to improve the matching cost computation stage with hybrid CNN-based combined with truncated directional intensity computation. The difference in truncated directional intensity value is employed to decrease radiometric errors. The proposed method’s raw matching cost went through the cost aggregation step using the bilateral filter (BF) to improve accuracy. The winner-take-all (WTA) optimization uses the aggregated cost volume to produce an initial disparity map. Finally, a series of refinement processes enhance the initial disparity map for a more accurate final disparity map. This paper verified the performance of the algorithm using the Middlebury online stereo benchmarking system. The proposed algorithm achieves the objective of generating a more accurate and smooth disparity map with different depths at low texture regions through better matching cost quality.

3D-Modeling Depth Optimization Using a Neural Network and Image Segments

2013 ◽  
Vol 333-335 ◽  
pp. 1096-1105 ◽  
Author(s):  
Fan Jun Liu ◽  
Bin Gang Cao
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Fourier Transformation ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Reference Image ◽  
Fast Fourier Transformation ◽  
Stereo Pair ◽  
Disparity Map ◽  
Ground Truth Data

We present a 3D(three-dimensional)-modeling disparity-map optimization algorithm using a neural network and image segments for stereo navigation. We decompose the optimization algorithm problem into two sub-problems: initial stereo matching and depth optimization. A two-step procedure is proposed to solve the sub-problems sequentially. The first step is a region based NCC(normalized cross-correlation) matching process. But we use fast Fourier transformation and inverse fast Fourier transformation to eliminate redundant calculations in NCC, and we create a high-confidence disparity map by cross checking. In the second step, the reference image (the left image of the inputted stereo pair) is segmented into regions according to homogeneous color. A neural network is then built to model the three dimensional surface and applied to refine disparities in each image segment. The experimental results obtained for Middlebury test datasets and real stereo road images indicate that our method is competitive with the best stereo matching algorithms currently available. In particular, the approach has significantly improved performance for road images used in navigation and the disparity maps recovered by our algorithm are similar to ground truth data.

scholarly journals QUANTITATIVE COMPARISON BETWEEN NEURAL NETWORK- AND SGM-BASED STEREO MATCHING

2019 ◽  
Vol XLII-1/W2 ◽  
pp. 17-22
Author(s):  
A. Frenzel ◽  
N. Deckers ◽  
R. Reulke
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Correspondence Analysis ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Quantitative Comparison ◽  
Depth Information ◽  
Depth Maps ◽  
Modern Methods

<p><strong>Abstract.</strong> Over the last decades, various methods for three-dimensional detection of the environment have been developed and successfully used. This work considers classical stereo methods, which can determine depth information by the means of correspondence analysis on the basis of two pictures of a scene. Recently, neural networks have been used to solve correspondence analysis. These procedures came first places on corresponding benchmarks and are ahead of many already established solutions. In this work, images captured by the ZED camera are evaluated for accuracy of the depth maps generated by several approaches. This includes modern methods based on neural networks.</p>

Digitalization system of ancient architecture decoration art based on neural network and image features

2020 ◽  
pp. 1-12
Author(s):  
Wu Xin ◽  
Qiu Daping
Keyword(s):  
Neural Network ◽  
Construction Industry ◽  
Three Dimensional ◽  
Performance Testing ◽  
Image Features ◽  
Three Dimensional Model ◽  
Performance Effect ◽  
Data Process ◽  
And Performance ◽  
Construction Mode

The inheritance and innovation of ancient architecture decoration art is an important way for the development of the construction industry. The data process of traditional ancient architecture decoration art is relatively backward, which leads to the obvious distortion of the digitalization of ancient architecture decoration art. In order to improve the digital effect of ancient architecture decoration art, based on neural network, this paper combines the image features to construct a neural network-based ancient architecture decoration art data system model, and graphically expresses the static construction mode and dynamic construction process of the architecture group. Based on this, three-dimensional model reconstruction and scene simulation experiments of architecture groups are realized. In order to verify the performance effect of the system proposed in this paper, it is verified through simulation and performance testing, and data visualization is performed through statistical methods. The result of the study shows that the digitalization effect of the ancient architecture decoration art proposed in this paper is good.

Operational Determination of the Activated Sludge Process Using Neural Networks

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2461-2464 ◽  
Author(s):  
R. D. Tyagi ◽  
Y. G. Du
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Steady State ◽  
Activated Sludge ◽  
Feedforward Neural Network ◽  
Training Data ◽  
Activated Sludge Process

A steady-statemathematical model of an activated sludgeprocess with a secondary settler was developed. With a limited number of training data samples obtained from the simulation at steady state, a feedforward neural network was established which exhibits an excellent capability for the operational prediction and determination.

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