Abstract
This paper proposes a map representation method of three-dimensional (3D) environment by using B-spline surfaces, which are first used to describe large environment in 3D map construction research. Initially, a 3D point cloud map is constructed based on extracted line segments with two mutually perpendicular 2D laser range finders (LRFs). Then two types of accumulated data sets are separated from the point cloud map according to different types of robot movements, continuous translation and continuous rotation. To express the environment more accurately, B-spline surface with covariance matrix is proposed to be extracted from each data set. Due to the random movements, there must be overlap between extracted B-spline surfaces. However, merging of two overlapping B-spline surfaces with different distribution directions of their control points is a complex problem, which is not well addressed by far. In our proposed method, each surface is divided into overlap and nonoverlap. Then generated sample points with propagated uncertainties from one overlap and their projection points located on the other overlap are merged using the product of Gaussian probability density functions. Based on this merged data set, a new surface is extracted to represent the environment instead of the two overlaps. Finally, proposed methods are validated by using the experimental result of an accurate representation of an indoor environment with B-spline surfaces.
Highlights
Two-dimensional (2D) features-based simultaneous localization and mapping (SLAM) is the problem of correcting a robot position and building an environment map by using the extracted features in unknown environment
Based on constructed 3D point cloud map, navigation [12] and path planning [13] research have been done in a 3D environment
In order to correctly localize the mobile robot and accurately build the 2D environment map, a data association method should be used to construct the correspondence between the stored line segments and the new extracted ones
Summary
Two-dimensional (2D) features-based simultaneous localization and mapping (SLAM) is the problem of correcting a robot position and building an environment map by using the extracted features in unknown environment. The parameters of a line segment are expressed in a local coordinate system of mobile robot because the sensor scan is obtained in the local reference frame To derive these parameters, the distance between the raw data points and the expected line segment is minimized. In order to correctly localize the mobile robot and accurately build the 2D environment map, a data association method should be used to construct the correspondence between the stored line segments and the new extracted ones. Based on the updated robot position, the raw data points obtained from the vertical sensor are projected into the 3D space to build the 3D point cloud map. The spacing distance is 100 mm and the rotation angle is 0.087 rad in each time
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