A two-step filtering approach for indoor lidar point clouds: efficient removal of jump points and misdetected points

In the simultaneous localization and mapping (SLAM) process of indoor mobile robots, the accuracy and stability of point cloud data are crucial for localization and environment perception. However, in practical applications, indoor mobile robots may encounter glass, smooth floors, edge objects, etc. Point cloud data in such environments are often misdetected, especially in the intersection of flat surfaces and the edge of obstacles, which are prone to generating jump points; in the smooth planes due to reflective properties or sensor errors, which may also lead to the emergence of misdetected points.To solve these problems, a two-step filtering method is proposed in this paper. In the first step, a clustering filtering algorithm based on radial distance and tangential span is used for effective filtering against jump points. The algorithm accurately identifies and filters out the jump points by analyzing the spatial relationship between each point in the point cloud and the neighboring points to ensure the accuracy of the data. In the second step, the filtering algorithm based on the grid penetration model is used to further filter out the misdetected points on the smooth plane. The model eliminates unrealistic point cloud data and improves the overall quality of the point cloud by simulating the characteristics of the beam penetrating the object. Experimental results in indoor environments show that this two-step filtering method significantly reduces the jump points and misdetected points in the point cloud and improves the navigation accuracy and stability of indoor mobile robots.