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?


def baseline_model():
    model = Sequential()
    model.add(Dense(2048, input_dim=160, activation='relu'))
    model.add(Dense(1024, activation='relu'))
    model.add(Dense(512, activation='relu'))
    model.add(Dense(256, activation='relu'))
    model.add(Dense(128, activation='relu'))
    model.add(Dense(64, activation='relu'))
    model.add(Dense(32, activation='relu'))
    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'])
  • $\begingroup$ 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. $\endgroup$ Dec 8, 2019 at 20:06

1 Answer 1


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

  • $\begingroup$ 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. $\endgroup$
    – Community Bot
    Apr 12 at 15:32
  • $\begingroup$ Am confused - the researchgate link is the citation $\endgroup$ Apr 16 at 11:09

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