In view of the low robustness and poor accuracy of traditional visual odometry in dynamic environment, a dense visual odometry based on edge information fusion was proposed. First, the spatial coordinates of pixels based on depth information were calculated, and the K-means algorithm was adopted for scene clustering. Based on the clustering of photometric information and edge information, the photometric consistency error and edge alignment error were constructed respectively, and the residual model was obtained after fusion and regularization of the two errors. Next, the average background depth was introduced to the residual model so as to expand the residual difference between the dynamic and static parts, ensuring correct motion segmentation. Then, a non-parametric statistical model was constructed based on the general characteristics of the cluster residual distribution, and motion segmentation was performed through dynamic thresholds to eliminate dynamic objects and obtain clustering weights. Finally, the weighted cluster residuals were added to the nonlinear optimization function of pose estimation to reduce the effect of dynamic objects and improve the accuracy of pose estimation. Experiments on TUM dataset show that the proposed algorithm could achieve better results in both static and high dynamic environments, and it had higher accuracy and robustness than the existing algorithm in dynamic environment.