Answers.Body: <p>According to this paper <a href="https://www.researchgate.net/publication/321816644_Deep_learning_for_2D_scan_matching_and_loop_closure" rel="nofollow noreferrer">https://www.researchgate.net/publication/321816644_Deep_learning_for_2D_scan_matching_and_loop_closure</a> the 2x scan goes through the following layers</p>
  <p><a href="https://i.sstatic.net/qe4Cq.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/qe4Cq.png" alt="conv maxpooling relu network" /></a></p>
  
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Answers.Text: Am confused - the researchgate link is the citation
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Body: <p>We are using 2D Laser Scanner to scan various objects of different geometric shapes for e.g. cylinder, spiked, cylinder with notch, cylinder with curved edges e.t.c. The dataset contains points in the format [x, y] with the dimension of 1 complete scan being 160x2. The goal is to use these scan points to classify the various shapes.</p>
  
  <p>I have used a multilayer NN with sigmoid as the final layer and Adadelta optimizer for this problem but the accuracy reaches only upto 70%. </p>
  
  <p>Can anyone recommend a proper model that can be used for Laser Scanner Data Classification?</p>
  
  <hr>
  
  <h2>          MODEL</h2>
  
  <pre><code>def baseline_model():
      model = Sequential()
      model.add(Dense(2048, input_dim=160, activation='relu'))
      model.add(Dropout(0.1))
      model.add(Dense(1024, activation='relu'))
      model.add(Dropout(0.1))
      model.add(Dense(512, activation='relu'))
      model.add(Dropout(0.2))
      model.add(Dense(256, activation='relu'))
      model.add(Dropout(0.3))
      model.add(Dense(128, activation='relu'))
      model.add(Dropout(0.4))
      model.add(Dense(64, activation='relu'))
      model.add(Dropout(0.5))
      model.add(Dense(32, activation='relu'))
      model.add(Dropout(0.5))
      model.add(Dense(6, activation='softmax'))
      Adam = optimizers.Adam(lr=0.001)
      Adadelta =  optimizers.Adadelta(lr = 1)
      model.compile(loss='categorical_crossentropy', optimizer=Adadelta,   metrics=['accuracy'])
  </code></pre>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-12-08T20:06:23.750Z
Comments.Text: You could convert this problem to shape detection on an image, where you can probably use few-layer convolution networks to solve the problem. You would introduce a lot of new variables but it would be easier to solve it that way.
Comments.UserDisplayName: Harsh Sinha
Tags: <neural-networks><keras><multilayer-perceptrons>
Title: Applying Machine Learning to 2D Laser Scanner Data
UserDisplayName: PMu
ThreadText: # Applying Machine Learning to 2D Laser Scanner Data
  
  (Asked by: username_0 on 2019-12-06)
  
  We are using 2D Laser Scanner to scan various objects of different geometric shapes for e.g. cylinder, spiked, cylinder with notch, cylinder with curved edges e.t.c. The dataset contains points in the format [x, y] with the dimension of 1 complete scan being 160x2. The goal is to use these scan points to classify the various shapes.
  I have used a multilayer NN with sigmoid as the final layer and Adadelta optimizer for this problem but the accuracy reaches only upto 70%. 
  Can anyone recommend a proper model that can be used for Laser Scanner Data Classification?
  
            MODEL
  def baseline_model():
      model = Sequential()
      model.add(Dense(2048, input_dim=160, activation='relu'))
      model.add(Dropout(0.1))
      model.add(Dense(1024, activation='relu'))
      model.add(Dropout(0.1))
      model.add(Dense(512, activation='relu'))
      model.add(Dropout(0.2))
      model.add(Dense(256, activation='relu'))
      model.add(Dropout(0.3))
      model.add(Dense(128, activation='relu'))
      model.add(Dropout(0.4))
      model.add(Dense(64, activation='relu'))
      model.add(Dropout(0.5))
      model.add(Dense(32, activation='relu'))
      model.add(Dropout(0.5))
      model.add(Dense(6, activation='softmax'))
      Adam = optimizers.Adam(lr=0.001)
      Adadelta =  optimizers.Adadelta(lr = 1)
      model.compile(loss='categorical_crossentropy', optimizer=Adadelta,   metrics=['accuracy'])
  
  **Tags:** neural-networks, keras, multilayer-perceptrons
  > **Comments:**
  > **username_1**: You could convert this problem to shape detection on an image, where you can probably use few-layer convolution networks to solve the problem. You would introduce a lot of new variables but it would be easier to solve it that way.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2023-04-12. Score: 0
  
  According to this paper <https://www.researchgate.net/publication/321816644_Deep_learning_for_2D_scan_matching_and_loop_closure> the 2x scan goes through the following layers
  
  [![conv maxpooling relu network](https://i.sstatic.net/qe4Cq.png)](https://i.sstatic.net/qe4Cq.png)
  
  > **Comments:**
  > **username_3**: Your answer could be improved with additional supporting information. Please [edit] to add further details, such as citations or documentation, so that others can confirm that your answer is correct. You can find more information on how to write good answers [in the help center](/help/how-to-answer).  
  > **username_2**: Am confused - the researchgate link is the citation  
  
  ---
  
  