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FastSLAM Algorithm As interpreted by Aaron Lee. Figure 13.2 For each particle: – “Retrieval”/”Prediction” Move particle – “Measurement update” For each.

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Presentation on theme: "FastSLAM Algorithm As interpreted by Aaron Lee. Figure 13.2 For each particle: – “Retrieval”/”Prediction” Move particle – “Measurement update” For each."— Presentation transcript:

1 FastSLAM Algorithm As interpreted by Aaron Lee

2 Figure 13.2 For each particle: – “Retrieval”/”Prediction” Move particle – “Measurement update” For each feature seen by the particle, update its mean and covariance – “Importance weight” Calculate how likely this particle is Resample Duplicate and delete particles based on goodness

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4 Get next particle

5 Move particle

6 Get next particle Move particle Loop through all features in the particle and determine how well each of them correlate with the measurement.

7 Get next particle Move particle Loop through all features in the particle and determine how well each of them correlate with the measurement. Set default importance if new feature

8 Get next particle Move particle Loop through all features in the particle and determine how well each of them correlate with the measurement. Set default importance if new feature Pick the feature that corresponds best to the measurement

9 Get next particle Move particle Loop through all features in the particle and determine how well each of them correlate with the measurement. Set default importance if new feature Pick the feature that corresponds best to the measurement If is a new feature, increase number by 1

10 Get next particle Move particle Loop through all features in the particle and determine how well each of them correlate with the measurement. Set default importance if new feature Pick the feature that corresponds best to the measurement If is a new feature, increase number by 1 If new feature, set mean to what measurement said. Remember that we have seen this feature once

11 Get next particle Move particle Loop through all features in the particle and determine how well each of them correlate with the measurement. Set default importance if new feature Pick the feature that corresponds best to the measurement If is a new feature, increase number by 1 If new feature, set mean to what measurement said. Remember that we have seen this feature once If we have seen this before, update mean and covariance. We have seen this feature once more.

12 If we didn’t see the feature, don’t change its properties

13 If we didn’t see the feature, don’t change its properties However, if we should have seen it, remember that we should have and didn’t

14 If we didn’t see the feature, don’t change its properties However, if we should have seen it, remember that we should have and didn’t If we see a feature less than 50% of the time then throw it away

15 If we didn’t see the feature, don’t change its properties However, if we should have seen it, remember that we should have and didn’t If we see a feature less than 50% of the time then throw it away We are done with this particle, so move it to temporary storage place

16 If we didn’t see the feature, don’t change its properties However, if we should have seen it, remember that we should have and didn’t If we see a feature less than 50% of the time then throw it away We are done with this particle, so move it to temporary storage place Empty out main particle storage place

17 If we didn’t see the feature, don’t change its properties However, if we should have seen it, remember that we should have and didn’t If we see a feature less than 50% of the time then throw it away We are done with this particle, so move it to temporary storage place Empty out main particle storage place Now we resample. This involves selecting M particles to copy from the temporary storage place to the main storage. The probability of any given particle of getting selected is based on its importance factor. w=target/proposal

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