Abstract
Due to GPS restrictions, an inertial sensor is usually used to estimate the location of indoor mobile robots. However, it is difficult to achieve high-accuracy localization and control by inertial sensors alone. In this paper, a new method is proposed to estimate an indoor mobile robot pose with six degrees of freedom based on an improved 3D-Normal Distributions Transform algorithm (3D-NDT). First, point cloud data are captured by a Kinect sensor and segmented according to the distance to the robot. After the segmentation, the input point cloud data are processed by the Approximate Voxel Grid Filter algorithm in different sized voxel grids. Second, the initial registration and precise registration are performed respectively according to the distance to the sensor. The most distant point cloud data use the 3D-Normal Distributions Transform algorithm (3D-NDT) with large-sized voxel grids for initial registration, based on the transformation matrix from the odometry method. The closest point cloud data use the 3D-NDT algorithm with small-sized voxel grids for precise registration. After the registrations above, a final transformation matrix is obtained and coordinated. Based on this transformation matrix, the pose estimation problem of the indoor mobile robot is solved. Test results show that this method can obtain accurate robot pose estimation and has better robustness.
Highlights
Simultaneous Localization and Mapping (SLAM) has long been a research topic in the field of robotics, with pose estimation one of the key subjects in SLAM research [1]
To solve the problem of indoor mobile robot localization, a pose estimation method based on an improved 3D-Normal Distributions Transform algorithm (3D-Normal Distributions Transform (NDT)) algorithm was proposed in this paper
On the basis of the normal 3D-NDT algorithm and the Approximate Voxel Grid Filter, the improved algorithm adopted the point cloud segmentation based on Euclidean distance and the size of the voxel grid was adapted to the distribution of point clouds
Summary
Simultaneous Localization and Mapping (SLAM) has long been a research topic in the field of robotics, with pose estimation one of the key subjects in SLAM research [1]. A mobile robot pose estimation method based on the improved 3D-NDT point cloud registration algorithm is proposed in this paper. The distant point cloud data use the 3D-Normal Distributions Transform algorithm (3D-NDT) with large-sized voxel grids for initial registration based on the transformation matrix from the odometry method. This improved method could avoid any influence on the subsequent registration algorithm caused by the wrong transformation matrix and could improve the robustness of the point cloud registration. Based on these improvements, the new method can obtain the pose estimation of a mobile robot accurately in a short time. If the size of voxel grids is too small, some input point cloud data may not find their corresponding voxel grids and these point cloud data cannot correct the position of the registration, which will increase the running time of the algorithm
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