In order to realize the accurate navigation of visual odometers in the large-scale complex environment and eliminate the cumulative error of pose estimation, this paper presents a method of visual loop closure detection and pose optimization based on geometric constraints.Firstly, a Bag of Visual Words(BoVW) based on LATCH descriptor is set up, and the loop closure detection method is adopted with the images described by the visual word vector and the similarity degree normalized.Then, the loop closure between the candidate keyframe and the current keyframe is checked based on RANSAC-HORN motion estimation.Finally, the local map points of the loop closure keyframe are projected to the current frame, and the re-projection error is minimized to optimize the pose.The experimental results show that the loop closure detection and pose optimization algorithm proposed in this paper can effectively and accurately detect and verify the loop closure, carry out the loop closure pose optimization of the error accumulation of the visual odometer and improve the accuracy of visual navigation.