In this paper, we propose the hybrid locally kernel-based weighted least square (HKLS) method to reduce the noise of the range images captured from the Microsoft Kinect sensor. Although removing the noise of Kinect sensor is inevitable in mobile robot applications, preserving the main structures of an indoor scene is a key point of the smoothing methods for range data. Our method uses a situation estimation module to apply an appropriate method for denoising the input data. In this paper, situations are classified into two categories including flat regions and edge regions. The locally kernel-based weighted least square method is used for the edge regions that have significant changes in the surface normals and the weight of each point is assigned with the Gaussian mixture model. The Bilateral filtering is applied for the flat regions with no sharp change in surface normals. We provided a solution for the noise data issue and proof of uncertainty reduction in presence of noise data. There is a fusion module for assigning the proportion of each method according to the different situations. We evaluated our method on the NYUv2 dataset that contains different RGB-D indoor scenes with other state-of-the-art methods. We achieved the value of 49.48 for the Z-MAE and 0.502 for the N-MAE error metrics

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