## 1 Introduction  
The relevant technologies for systems such as unmanned vehicles and intelligent mobile robots have been rapidly developed in recent years [1], in which the environment perception technology is one of the key technologies to ensure their safety. In environment perception, Lidar is a sensor that directly acquires three-dimensional information. It can capture the surrounding objects, obstacles, and road conditions with high-precision and high-resolution point cloud data, making it one of the key sensors in unmanned environment perception. The investigation of environmental perception, recognition, and analysis through the utilization of Lidar point cloud data has gained significant importance [2, 3].  
Generally, dynamic objects such as pedestrians, bicycles, and automobiles in the road environment occupy only a very small spatial area within the detectable range of Lidar, and most of the area is covered by static scene elements such as buildings, roads, and vegetation. Whereas the detection of moving objects is essential for unmanned vehicles and intelligent mobile robots, the recognition of these moving objects and the estimation of their motion states are equally, if not more, critical than the identification of static scene elements. There is an increasing body of research focused on the segmentation of moving objects [4] and scene flow estimation [5] for Lidar point clouds. However, these two aspects are typically addressed in isolation. For instance, the moving object segmentation algorithm solely performs the task of identifying moving entities, while the scene flow estimation algorithm does not differentiate between moving and stationary objects but instead estimates motion vectors for scene points by utilizing point clouds from both the previous and current frames. Predicting motion vectors directly using scene flow estimation methods would require a lot of effort for the vast majority of non-motion points in the scene. This is because, in a point cloud scanning scene, only a small portion of the point clouds are covered by moving objects, while the majority are covered by static scene elements such as buildings, roads, and vegetation. This creates a problem of imbalance between dynamic and static data. While motion targets have equal or even higher demands, directly predicting motion vectors using scene flow estimation will lead to a large number of point clouds in consecutive frames and consume a large amount of resources.  
Therefore, in order to estimate the object motion state and obtain accurate motion vectors with a moving platform for practical applications, this paper combines motion segmentation and scene flow estimation techniques to divide the moving object vector estimation problem into two phases, that is, multi-frame-based moving object segmentation and moving object vector estimation. To address the issue of relative motion concerning stationary objects caused by the ego-motion of the Lidar sensor, we first compensate for the platform's ego-motion. Subsequently, moving objects within the scene are segmented using an enhanced motion segmentation model, followed by estimating motion vectors for these segmented objects through spatio-temporal clustering fusion.  
The remainder of this paper is organized as follows: Section 2 introduces the related works. The proposed motion state estimation method is presented in detail in Section 3. Section 4 shows the experimental results, and the conclusions follow in Section 5.