Establishing a 3D model of an underground environment for an inspection robot has received significant attention and concern in recent years. RGB and depth images are obtained using a depth camera. The acquired RGB and depth maps are filtered to remove noise points using a Markov random field (MRF)-based filter. A novel deep neural network (DNN) architecture that implements the feature description is proposed. The feature points of a depth image are extracted to realize the precise matching between the RGB and depth images. Point clouds are obtained and registered into a single position using an improved iterative closest point algorithm. The experimental results show the effectiveness and practicability of the proposed method. An accurate 3D reconstruction of the object has been achieved with a dense point cloud.