Dense reconstruction is a significant component of visual simultaneous localization and mapping（ SLAM）. Accurate measurement of depth distance of each pixel point plays a crucial role in dense reconstruction.In industrial applications

the RGB-D camera is generally utilized for dense reconstruction; however

the RGB-D camera still remain certain unavoidable shortcomings

i. e.

small measuring range

narrow application scope

and high sensitivity to illumination. Hence

the depth estimation of the monocular camera with filter mode not only guarantees the real-time requirement of SLAM

but also be suitable for outdoor and large scenarios. A cluster-based uniform Gauss distributed depth filtering algorithm is used to overcome the difficulty that the dense reconstruction of the deep filtering under the Gauss distribution hypothesis with an unsatisfactory accuracy. The improved algorithm is adopted to deal with the mismatched pixels point

and also significantly robust against outliers. In addition

on the basis of correctly processing the outliers data

the problem of depth value estimation error and big difference of adjacent pixel depth value is solved. The experimental results show that the improved depth estimation algorithm is more delicate in reconstruction of dense maps

and the reconstruction accuracy is improved by about30 %.