This module finds the optimal number of components (number of clusters) for a given dataset. In order to find the optimal number of components for, first we used k-means algorithm with a different number of clusters, starting from 1 to a fixed max number. Then we checked the cluster validity by deploying \( C-index \) algorithm and […]
Finding optimal number of Clusters by using Cluster validation Read More »
I was looking for a way to create an octomap tree from arbitrary mesh file. Well at first I tried to convert my data into PCL pointcloud and then convert them into an octomap tree. But the problem was, for instance when you a cuboid mesh, you only have 8 vertex, which gives you 8 point and the walls between
How to create octomap tree from mesh Read More »
In this tutorial, I explain the concept, probabilistic sensor fusion model and the sensor model used in Octomap library. related publication: OctoMap: An Efficient Probabilistic 3D Mapping Framework Based on Octrees 1)Octamap Volumetric Model 2)Probabilistic Sensor Fusion Model 3)Sensor Model for Laser Range Data
Octomap explanierend Read More »
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/* http://planning.cs.uiuc.edu/node103.html |r11 r12 r13 | |r21 r22 r23 | |r31 r32 r33 | yaw: alpha=arctan(r21/r11) pitch: beta=arctan(-r31/sqrt( r32^2+r33^2 ) ) roll: gamma=arctan(r32/r33) */ double roll, pitch, yaw; roll=M_PI/3; pitch=M_PI/4; yaw=M_PI/6; Eigen::AngleAxisd rollAngle(roll, Eigen::Vector3d::UnitX()); Eigen::AngleAxisd pitchAngle(pitch, Eigen::Vector3d::UnitY()); Eigen::AngleAxisd yawAngle(yaw, Eigen::Vector3d::UnitZ()); Eigen::Quaternion<double> q = yawAngle*pitchAngle *rollAngle; Eigen::Matrix3d rotationMatrix = q.matrix(); std::cout<<"roll is Pi/" <<M_PI/atan2( rotationMatrix(2,1),rotationMatrix(2,2) ) <<std::endl; std::cout<<"pitch: Pi/" <<M_PI/atan2( -rotationMatrix(2,0), std::pow( rotationMatrix(2,1)*rotationMatrix(2,1) +rotationMatrix(2,2)*rotationMatrix(2,2) ,0.5 ) ) <<std::endl; std::cout<<"yaw is Pi/" <<M_PI/atan2( rotationMatrix(1,0),rotationMatrix(0,0) ) <<std::endl; |
Finding roll, pitch yaw from 3X3 rotation matrix with Eigen Read More »
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/* yaw: A yaw is a counterclockwise rotation of alpha about the z-axis. The rotation matrix is given by R_z |cos(alpha) -sin(alpha) 0| |sin(apha) cos(alpha) 0| | 0 0 1| pitch: R_y A pitch is a counterclockwise rotation of beta about the y-axis. The rotation matrix is given by |cos(beta) 0 sin(beta)| |0 1 0 | |-sin(beta) 0 cos(beta)| roll: A roll is a counterclockwise rotation of gamma about the x-axis. The rotation matrix is given by R_x |1 0 0| |0 cos(gamma) -sin(gamma)| |0 sin(gamma) cos(gamma)| It is important to note that R_z R_y R_x performs the roll first, then the pitch, and finally the yaw Roration matrix: R_z*R_y*R_x */ double roll, pitch, yaw; roll=M_PI/3; pitch=M_PI/4; yaw=M_PI/6; Eigen::AngleAxisd rollAngle(roll, Eigen::Vector3d::UnitX()); Eigen::AngleAxisd pitchAngle(pitch, Eigen::Vector3d::UnitY()); Eigen::AngleAxisd yawAngle(yaw, Eigen::Vector3d::UnitZ()); Eigen::Quaternion<double> q = yawAngle*pitchAngle *rollAngle; Eigen::Matrix3d rotationMatrix = q.matrix(); std::cout<<rotationMatrix <<std::endl; |
From Eigen documentation: If you are working with OpenGL 4×4 matrices then Affine3f and Affine3d are what you want. Since Eigen defaults to column-major storage, you can directly use the Transform::data() method to pass your transformation matrix to OpenGL. construct a Transform:
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Transform t(AngleAxis(angle,axis)); |
or like this:
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Transform t; t = AngleAxis(angle,axis); |
But note that unfortunately, because of
Roll, pitch, yaw using Eigen and KDL Frame Read More »
Gaussian Mixture Regression is basically Multivariate normal distribution with Conditional distribution. The more about the theory could be found at [1], [2], [3], [4]. For this work, I have added the functionality of adding Gaussian Mixture Regression to this project on the GitHub by forking the main project, my forked project can be download at here Github The main changes
Gaussian Mixture Regression Read More »
I found a really good code at GitHub for fitting a Gaussian Mixture Model (GMM) with Expectation Maximization (EM) for ROS. There are so many parameters that you can change. Some of the most important ones are:
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// minimum number of gaussians #define PARAM_NAME_GAUSSIAN_COUNT_MIN "gaussian_count_min" #define PARAM_DEFAULT_GAUSSIAN_COUNT_MIN 1 // search will terminate when the gaussian count reaches this #define PARAM_NAME_GAUSSIAN_COUNT_MAX "gaussian_count_max" #define PARAM_DEFAULT_GAUSSIAN_COUNT_MAX 10 |
To find the optimal number of components, it uses Bayesian information criterion (BIC). There are other methods to find
Expectation Maximization algorithm to obtain Gaussian mixture models for ROS Read More »
This is the part I of the work that I did for my master thesis (part II). In this work first, I computed HOG (Histogram of oriented gradients) on my images and then sent the computed histogram to a linear SVM (support vector machine). The SVM was trained with human and non-human images. The output of the classifier was
Human detection on mobile camera using HOG and tracking them using Kalman filter Read More »
Most of the wheeled robots in ROS use move_base to move the robot. move_base geometry model is based on differential drive which basically transforms a velocity command (twist message) into a command for rotating the left and the right wheels at a different speed which enable the car to turn into the right or left or goes straight. But cars
Ackermann steering car robot model with simulation in Gazebo Read More »
Installation Please visit the official documentation page but do not forget to add the permission problem:
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sudo usermod -a -G docker $USER |
Docker image If the image doesn’t exist it will pull it from hub.docker.com Basically, it runs the pull command beforehand:
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docker pull <image_name:tag_name> |
for instance, to install Ubuntu 20.04
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docker pull ubuntu:20.04 |
to check the version, just run:
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lsb_release -a |
To see available
