Commented to avoid conflict with ros environment

Follow the steps below to capture the features, and train the SVM.

Put the cloned repository (sensor_stick) in the following location assuming the workspace is in home directory of the user:
```
cp sensor_stick ~/catkin_ws_1/src
```
Change directory to catkin workspace:
```
cd ~/catkin_ws_1
```

Install the dependacies of the project for the ros using rosdep:

rosdep install --from-paths src --ignore-src --rosdistro=kinetic -y

Build the catkin workspace:
```
catkin_make
```
Ensure that the following (similar) lines are appropriately present in the ~/.bashrc file, and if not, add them appropriately and save the file:
```
source /opt/ros/kinetic/setup.bash
source ~/catkin_ws_1/devel/setup.bash
export GAZEBO_MODEL_PATH=~/catkin_ws_1/src/sensor_stick/models
```
Commented to avoid conflict with ros environment
```
#export PATH="/home/sachin/miniconda3/bin:$PATH"
```
Close the terminal and reopen a new terminal, and change to the catkin workspace:
```
cd ~/catkin_ws_1
```
Capture the features:

7.1. Run the following command to launch the gaebo environment for training:

 roslaunch sensor_stick training.launch

7.2. Open a new terminal, and change directory to catkin workspace, and then start the feature extraction process:

 cd ~/catkin_ws_1
rosrun sensor_stick capture_features.py

This should start the feature extraction process on 100 random samples for each of the 6 objects. Wait for the process to complete. After the process is complete the capture_features.py script will end automatically. A file named training_set.sav should be created in the catkin workspace directory. Close the training.launch process also after this.

Train the SVM:
8.1. Ensure to be in catkin workspace:
```
 cd ~/catkin_ws_1
```
8.2. Train the SVM using the features extracted previously;
```
 rosrun sensor_stick train_svm.py
```

This should create a file model.sav in the catkin workspace, which can be shared.

(Optional) If more training features are required, do the following:

9.1. Open up the following file using a editor:

 vi ~/catkin_ws_1/src/sensor_stick/scripts/capture_features.py

Change the number of random samples for training to 1000 from 100. Save the file.

9.2. Follow step 7 and 8 to generate the model.sav which can be shared.

Follow the steps below to capture the features, and train the SVM.

Put the cloned repository (sensor_stick) in the following location assuming the workspace is in home directory of the user:
```
cp sensor_stick ~/catkin_ws_1/src
```
Change directory to catkin workspace:
```
cd ~/catkin_ws_1
```

Install the dependacies of the project for the ros using rosdep:

rosdep install --from-paths src --ignore-src --rosdistro=kinetic -y

Build the catkin workspace:
```
catkin_make
```
Ensure that the following (similar) lines are appropriately present in the ~/.bashrc file, and if not, add them appropriately and save the file:
```
source /opt/ros/kinetic/setup.bash
source ~/catkin_ws_1/devel/setup.bash
export GAZEBO_MODEL_PATH=~/catkin_ws_1/src/sensor_stick/models
```
Commented to avoid conflict with ros environment
```
#export PATH="/home/sachin/miniconda3/bin:$PATH"
```
Close the terminal and reopen a new terminal, and change to the catkin workspace:
```
cd ~/catkin_ws_1
```
Capture the features:

7.1. Run the following command to launch the gaebo environment for training:

 roslaunch sensor_stick training.launch

7.2. Open a new terminal, and change directory to catkin workspace, and then start the feature extraction process:

 cd ~/catkin_ws_1
rosrun sensor_stick capture_features.py

This should start the feature extraction process on 100 random samples for each of the 6 objects. Wait for the process to complete. After the process is complete the capture_features.py script will end automatically. A file named training_set.sav should be created in the catkin workspace directory. Close the training.launch process also after this.

Train the SVM:
8.1. Ensure to be in catkin workspace:
```
 cd ~/catkin_ws_1
```
8.2. Train the SVM using the features extracted previously;
```
 rosrun sensor_stick train_svm.py
```

This should create a file model.sav in the catkin workspace, which can be shared.

(Optional) If more training features are required, do the following:

9.1. Open up the following file using a editor:

 vi ~/catkin_ws_1/src/sensor_stick/scripts/capture_features.py

Change the number of random samples for training to 1000 from 100. Save the file.

9.2. Follow step 7 and 8 to generate the model.sav which can be shared.

Follow the steps below to capture the features, and train the SVM.

Put the cloned repository (sensor_stick) in the following location assuming the workspace is in home directory of the user:
```
cp sensor_stick ~/catkin_ws_1/src
```
Change directory to catkin workspace:
```
cd ~/catkin_ws_1
```

Install the dependacies of the project for the ros using rosdep:

rosdep install --from-paths src --ignore-src --rosdistro=kinetic -y

Build the catkin workspace:
```
catkin_make
```
Ensure that the following (similar) lines are appropriately present in the ~/.bashrc file, and if not, add them appropriately and save the file:
```
source /opt/ros/kinetic/setup.bash
source ~/catkin_ws_1/devel/setup.bash
export GAZEBO_MODEL_PATH=~/catkin_ws_1/src/sensor_stick/models
```
Commented to avoid conflict with ros environment
```
#export PATH="/home/sachin/miniconda3/bin:$PATH"
```
Close the terminal and reopen a new terminal, and change to the catkin workspace:
```
cd ~/catkin_ws_1
```
Capture the features:

7.1. Run the following command to launch the gaebo environment for training:

 roslaunch sensor_stick training.launch

7.2. Open a new terminal, and change directory to catkin workspace, and then start the feature extraction process:

 cd ~/catkin_ws_1
rosrun sensor_stick capture_features.py

This should start the feature extraction process on 100 random samples for each of the 6 objects. Wait for the process to complete. After the process is complete the capture_features.py script will end automatically. A file named training_set.sav should be created in the catkin workspace directory. Close the training.launch process also after this.

Train the SVM:
8.1. Ensure to be in catkin workspace:
```
 cd ~/catkin_ws_1
```
8.2. Train the SVM using the features extracted previously;
```
 rosrun sensor_stick train_svm.py
```

This should create a file model.sav in the catkin workspace, which can be shared.

(Optional) If more training features are required, do the following:

9.1. Open up the following file using a editor:

 vi ~/catkin_ws_1/src/sensor_stick/scripts/capture_features.py

Change the number of random samples for training to 1000 from 100. Save the file.

9.2. Follow step 7 and 8 to generate the model.sav which can be shared.

Follow the steps below to capture the features, and train the SVM.

Put the cloned repository (sensor_stick) in the following location assuming the workspace is in home directory of the user:
```
cp sensor_stick ~/catkin_ws_1/src
```
Change directory to catkin workspace:
```
cd ~/catkin_ws_1
```

Install the dependacies of the project for the ros using rosdep:

rosdep install --from-paths src --ignore-src --rosdistro=kinetic -y

Build the catkin workspace:
```
catkin_make
```
Ensure that the following (similar) lines are appropriately present in the ~/.bashrc file, and if not, add them appropriately and save the file:
```
source /opt/ros/kinetic/setup.bash
source ~/catkin_ws_1/devel/setup.bash
export GAZEBO_MODEL_PATH=~/catkin_ws_1/src/sensor_stick/models
```
Commented to avoid conflict with ros environment
```
#export PATH="/home/sachin/miniconda3/bin:$PATH"
```
Close the terminal and reopen a new terminal, and change to the catkin workspace:
```
cd ~/catkin_ws_1
```
Capture the features:

7.1. Run the following command to launch the gaebo environment for training:

 roslaunch sensor_stick training.launch

7.2. Open a new terminal, and change directory to catkin workspace, and then start the feature extraction process:

 cd ~/catkin_ws_1
rosrun sensor_stick capture_features.py

This should start the feature extraction process on 100 random samples for each of the 6 objects. Wait for the process to complete. After the process is complete the capture_features.py script will end automatically. A file named training_set.sav should be created in the catkin workspace directory. Close the training.launch process also after this.

Train the SVM:
8.1. Ensure to be in catkin workspace:
```
 cd ~/catkin_ws_1
```
8.2. Train the SVM using the features extracted previously;
```
 rosrun sensor_stick train_svm.py
```

This should create a file model.sav in the catkin workspace, which can be shared.

(Optional) If more training features are required, do the following:

9.1. Open up the following file using a editor:

 vi ~/catkin_ws_1/src/sensor_stick/scripts/capture_features.py

Change the number of random samples for training to 1000 from 100. Save the file.

9.2. Follow step 7 and 8 to generate the model.sav which can be shared.

Follow the steps below to capture the features, and train the SVM.

Put the cloned repository (sensor_stick) in the following location assuming the workspace is in home directory of the user:
```
cp sensor_stick ~/catkin_ws_1/src
```
Change directory to catkin workspace:
```
cd ~/catkin_ws_1
```

Install the dependacies of the project for the ros using rosdep:

rosdep install --from-paths src --ignore-src --rosdistro=kinetic -y

Build the catkin workspace:
```
catkin_make
```
Ensure that the following (similar) lines are appropriately present in the ~/.bashrc file, and if not, add them appropriately and save the file:
```
source /opt/ros/kinetic/setup.bash
source ~/catkin_ws_1/devel/setup.bash
export GAZEBO_MODEL_PATH=~/catkin_ws_1/src/sensor_stick/models
```
Commented to avoid conflict with ros environment
```
#export PATH="/home/sachin/miniconda3/bin:$PATH"
```
Close the terminal and reopen a new terminal, and change to the catkin workspace:
```
cd ~/catkin_ws_1
```
Capture the features:

7.1. Run the following command to launch the gaebo environment for training:

 roslaunch sensor_stick training.launch

7.2. Open a new terminal, and change directory to catkin workspace, and then start the feature extraction process:

 cd ~/catkin_ws_1
rosrun sensor_stick capture_features.py

This should start the feature extraction process on 100 random samples for each of the 6 objects. Wait for the process to complete. After the process is complete the capture_features.py script will end automatically. A file named training_set.sav should be created in the catkin workspace directory. Close the training.launch process also after this.

Train the SVM:
8.1. Ensure to be in catkin workspace:
```
 cd ~/catkin_ws_1
```
8.2. Train the SVM using the features extracted previously;
```
 rosrun sensor_stick train_svm.py
```

This should create a file model.sav in the catkin workspace, which can be shared.

(Optional) If more training features are required, do the following:

9.1. Open up the following file using a editor:

 vi ~/catkin_ws_1/src/sensor_stick/scripts/capture_features.py

Change the number of random samples for training to 1000 from 100. Save the file.

9.2. Follow step 7 and 8 to generate the model.sav which can be shared.

Follow the steps below to capture the features, and train the SVM.

Put the cloned repository (sensor_stick) in the following location assuming the workspace is in home directory of the user:
```
cp sensor_stick ~/catkin_ws_1/src
```
Change directory to catkin workspace:
```
cd ~/catkin_ws_1
```

Install the dependacies of the project for the ros using rosdep:

rosdep install --from-paths src --ignore-src --rosdistro=kinetic -y

Build the catkin workspace:
```
catkin_make
```
Ensure that the following (similar) lines are appropriately present in the ~/.bashrc file, and if not, add them appropriately and save the file:
```
source /opt/ros/kinetic/setup.bash
source ~/catkin_ws_1/devel/setup.bash
export GAZEBO_MODEL_PATH=~/catkin_ws_1/src/sensor_stick/models
```
Commented to avoid conflict with ros environment
```
#export PATH="/home/sachin/miniconda3/bin:$PATH"
```
Close the terminal and reopen a new terminal, and change to the catkin workspace:
```
cd ~/catkin_ws_1
```
Capture the features:

7.1. Run the following command to launch the gaebo environment for training:

 roslaunch sensor_stick training.launch

7.2. Open a new terminal, and change directory to catkin workspace, and then start the feature extraction process:

 cd ~/catkin_ws_1
rosrun sensor_stick capture_features.py

This should start the feature extraction process on 100 random samples for each of the 6 objects. Wait for the process to complete. After the process is complete the capture_features.py script will end automatically. A file named training_set.sav should be created in the catkin workspace directory. Close the training.launch process also after this.

Train the SVM:
8.1. Ensure to be in catkin workspace:
```
 cd ~/catkin_ws_1
```
8.2. Train the SVM using the features extracted previously;
```
 rosrun sensor_stick train_svm.py
```

This should create a file model.sav in the catkin workspace, which can be shared.

(Optional) If more training features are required, do the following:

9.1. Open up the following file using a editor:

 vi ~/catkin_ws_1/src/sensor_stick/scripts/capture_features.py

Change the number of random samples for training to 1000 from 100. Save the file.

9.2. Follow step 7 and 8 to generate the model.sav which can be shared.

Follow the steps below to capture the features, and train the SVM.

Put the cloned repository (sensor_stick) in the following location assuming the workspace is in home directory of the user:
```
cp sensor_stick ~/catkin_ws_1/src
```
Change directory to catkin workspace:
```
cd ~/catkin_ws_1
```

Install the dependacies of the project for the ros using rosdep:

rosdep install --from-paths src --ignore-src --rosdistro=kinetic -y

Build the catkin workspace:
```
catkin_make
```
Ensure that the following (similar) lines are appropriately present in the ~/.bashrc file, and if not, add them appropriately and save the file:
```
source /opt/ros/kinetic/setup.bash
source ~/catkin_ws_1/devel/setup.bash
export GAZEBO_MODEL_PATH=~/catkin_ws_1/src/sensor_stick/models
```
Commented to avoid conflict with ros environment
```
#export PATH="/home/sachin/miniconda3/bin:$PATH"
```
Close the terminal and reopen a new terminal, and change to the catkin workspace:
```
cd ~/catkin_ws_1
```
Capture the features:

7.1. Run the following command to launch the gaebo environment for training:

 roslaunch sensor_stick training.launch

7.2. Open a new terminal, and change directory to catkin workspace, and then start the feature extraction process:

 cd ~/catkin_ws_1
rosrun sensor_stick capture_features.py

This should start the feature extraction process on 100 random samples for each of the 6 objects. Wait for the process to complete. After the process is complete the capture_features.py script will end automatically. A file named training_set.sav should be created in the catkin workspace directory. Close the training.launch process also after this.

Train the SVM:
8.1. Ensure to be in catkin workspace:
```
 cd ~/catkin_ws_1
```
8.2. Train the SVM using the features extracted previously;
```
 rosrun sensor_stick train_svm.py
```

This should create a file model.sav in the catkin workspace, which can be shared.

(Optional) If more training features are required, do the following:

9.1. Open up the following file using a editor:

 vi ~/catkin_ws_1/src/sensor_stick/scripts/capture_features.py

Change the number of random samples for training to 1000 from 100. Save the file.

9.2. Follow step 7 and 8 to generate the model.sav which can be shared.

Follow the steps below to capture the features, and train the SVM.

Put the cloned repository (sensor_stick) in the following location assuming the workspace is in home directory of the user:
```
cp sensor_stick ~/catkin_ws_1/src
```
Change directory to catkin workspace:
```
cd ~/catkin_ws_1
```

Install the dependacies of the project for the ros using rosdep:

rosdep install --from-paths src --ignore-src --rosdistro=kinetic -y

Build the catkin workspace:
```
catkin_make
```
Ensure that the following (similar) lines are appropriately present in the ~/.bashrc file, and if not, add them appropriately and save the file:
```
source /opt/ros/kinetic/setup.bash
source ~/catkin_ws_1/devel/setup.bash
export GAZEBO_MODEL_PATH=~/catkin_ws_1/src/sensor_stick/models
```
Commented to avoid conflict with ros environment
```
#export PATH="/home/sachin/miniconda3/bin:$PATH"
```
Close the terminal and reopen a new terminal, and change to the catkin workspace:
```
cd ~/catkin_ws_1
```
Capture the features:

7.1. Run the following command to launch the gaebo environment for training:

 roslaunch sensor_stick training.launch

7.2. Open a new terminal, and change directory to catkin workspace, and then start the feature extraction process:

 cd ~/catkin_ws_1
rosrun sensor_stick capture_features.py

This should start the feature extraction process on 100 random samples for each of the 6 objects. Wait for the process to complete. After the process is complete the capture_features.py script will end automatically. A file named training_set.sav should be created in the catkin workspace directory. Close the training.launch process also after this.

Train the SVM:
8.1. Ensure to be in catkin workspace:
```
 cd ~/catkin_ws_1
```
8.2. Train the SVM using the features extracted previously;
```
 rosrun sensor_stick train_svm.py
```

This should create a file model.sav in the catkin workspace, which can be shared.

(Optional) If more training features are required, do the following:

9.1. Open up the following file using a editor:

 vi ~/catkin_ws_1/src/sensor_stick/scripts/capture_features.py

Change the number of random samples for training to 1000 from 100. Save the file.

9.2. Follow step 7 and 8 to generate the model.sav which can be shared.

Name		Name	Last commit message	Last commit date
config		config
generated_files		generated_files
launch		launch
meshes		meshes
models		models
msg		msg
scripts		scripts
src		src
srv		srv
urdf		urdf
worlds		worlds
.DS_Store		.DS_Store
CMakeLists.txt		CMakeLists.txt
Readme.md		Readme.md
package.xml		package.xml
setup.py		setup.py

Name		Name	Last commit message	Last commit date
config		config
generated_files		generated_files
launch		launch
meshes		meshes
models		models
msg		msg
scripts		scripts
src		src
srv		srv
urdf		urdf
worlds		worlds
.DS_Store		.DS_Store
CMakeLists.txt		CMakeLists.txt
Readme.md		Readme.md
package.xml		package.xml
setup.py		setup.py

Name		Name	Last commit message	Last commit date
config		config
generated_files		generated_files
launch		launch
meshes		meshes
models		models
msg		msg
scripts		scripts
src		src
srv		srv
urdf		urdf
worlds		worlds
.DS_Store		.DS_Store
CMakeLists.txt		CMakeLists.txt
Readme.md		Readme.md
package.xml		package.xml
setup.py		setup.py

Name		Name	Last commit message	Last commit date
config		config
generated_files		generated_files
launch		launch
meshes		meshes
models		models
msg		msg
scripts		scripts
src		src
srv		srv
urdf		urdf
worlds		worlds
.DS_Store		.DS_Store
CMakeLists.txt		CMakeLists.txt
Readme.md		Readme.md
package.xml		package.xml
setup.py		setup.py

Name		Name	Last commit message	Last commit date
config		config
generated_files		generated_files
launch		launch
meshes		meshes
models		models
msg		msg
scripts		scripts
src		src
srv		srv
urdf		urdf
worlds		worlds
.DS_Store		.DS_Store
CMakeLists.txt		CMakeLists.txt
Readme.md		Readme.md
package.xml		package.xml
setup.py		setup.py

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Commented to avoid conflict with ros environment

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Commented to avoid conflict with ros environment

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Commented to avoid conflict with ros environment

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