Update README.md

README.md

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 ### 4- Statistical Outlier Filtering
 While calibration takes care of distortion, noise due to external factors like dust in the environment, humidity in the air, or presence of various light sources lead to sparse outliers which corrupt the results even more.
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 Such outliers lead to complications in the estimation of point cloud characteristics like curvature, gradients, etc. leading to erroneous values, which in turn might cause failures at various stages in our perception pipeline.
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 One of the filtering techniques used to remove such outliers is to perform a statistical analysis in the neighborhood of each point, and remove those points which do not meet a certain criteria. PCL’s StatisticalOutlierRemoval filter is an example of one such filtering technique. For each point in the point cloud, it computes the distance to all of its neighbors, and then calculates a mean distance.
 
 ```python
@@ -134,6 +136,7 @@ big difference right!!
 
 ### 5- Voxel Grid Downsampling
 RGB-D cameras provide feature rich and particularly dense point clouds, meaning, more points are packed in per unit volume than, for example, a Lidar point cloud. Running computation on a full resolution point cloud can be slow and may not yield any improvement on results obtained using a more sparsely sampled point cloud.
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 So, in many cases, it is advantageous to downsample the data. In particular, you are going to use a VoxelGrid Downsampling Filter to derive a point cloud that has fewer points but should still do a good job of representing the input point cloud as a whole.
 ```python
 # using VoxelGrid Downsampling Filter to derive a point cloud that has fewer points