Spatial measurement method based on object shape and velocity information using past measurement information from 3D-LiDAR

Abstract

In this paper, we propose a spatial measurement method based on object geometry and velocity information using past measurement information from 3D-LiDAR. If geometric information is obtained from 3D-LiDAR, it can be applied to global localization, object identification, etc., and robots will be able to run autonomously with greater stability. However, 3D-LiDAR cannot always measure point densities high enough to acquire sufficient geometric information. Conventional methods have been used to control point density by interpolating areas of low point density or by varying the rotational scanning speed of 3D-LiDAR. However, these methods have problems such as the shape of the measured object differing from the real object and the loss of real-time measurement. In this paper, therefore, a remaining time is set for each point based on the shape information of the object, and each point is allowed to remain until the remaining time is reached, depending on the robot’s movement. The remaining time is set so that the point density is preferentially increased in areas where shape information is estimated to be abundant and in distant areas where the point density tends to decrease due to the LiDAR mechanism. For dynamic point clouds, velocity information is added, and the point cloud is moved to the estimated position the next time and superimposed on the latest dynamic point cloud while considering the distance between points, thereby improving point density while suppressing shape collapse. We demonstrate the usefulness of this method through experiments on actual equipment.

Keywords

• spatial measurement method
• 3d-lidar
• improvement of point density
• moving point cloud
• autonomous mobile robotics