Abstract: The robustness of the current monocular vision odometries isn't strong enough when the mobile robot performs an approximately pure rotation. For this reason, a monocular vision odometry algorithm based on the improved 3D ICP (iterative closest point) matching is proposed by theoretically analyzing the reason of the low localization robustness. Firstly, the depth of the edge-feature point of the image is initialized. Then, an improved 3D ICP algorithm is used to iteratively solve the 6D poses of the 3D point sets between two frames. Finally, the depth value is updated by the extended Kalman depth filter based on the geometric constraint relation of edge-feature points. The improved ICP algorithm is superior to the traditional ones in the real-time performance and the accuracy, by using the inverse depth uncertainty weighting, the edge-feature gradient search, the matching and so on. What's more, it can further improve the localization accuracy and the robustness in case of the approximately pure rotation, by taking the wheel-mounted odometer data as the iterative initial values. The algorithm is verified on three public datasets, and it can effectively improve the robustness under the condition of the approximately pure rotation or in a large scale without decline of the localization accuracy. The proposed algorithm can correct the drift of odometer data for an actual monocular mobile robot to a certain extent.