Intrinsic dimensionality estimation plays a pivotal role in dealing with high-dimensional datasets. In this work, we aim to develop a robust dimensionality estimation algorithm by investigating the intrinsic dimensionality estimation methods for data points in its local region. Our method is able to effectively utilise the geometric information in the local region for dimensionality. We also show different methods to improve the estimation by using perspectives from the local region and different preprocessing methods.