Research On 3D Mapping Of Indoor Mobile Robot Based On RGB-D Camera

High precision colored 3D point cloud map can describe the indoor environment in which the robot operates,which is the found ation of localization,navigation,virtual reality and augmented reality,and is also one of the most important requirements for human-computer interaction.Indoor mobile robot environment perception using Low-cost RGB-D sensors has become one of the research hotspots at home and abroad.Only if the robot has the ability to build a 3D scene map,it is possible to interact with people and perform complex tasks in the environment more independently.Therefore,3D mapping is the necessary condition for the service robot to realize intelligence.Point cloud registration and loop detection are key steps in building 3D model of the environment and estimating the robot pose.RGB-D cameras capture RGB images along with per-pixel depth information,but the depth estimates are low accuracy and noisy.Therefore,it is the immediate local neighborhoods of the detected local visual feature points that reduce the registration error and computation,but which is limited to the rich visual features.Relative to the point to point registration,the Three-Dimensional Normal Distributions Transform(3D-NDT)algorithm divide the point clouds into fixed 3D voxel grid cell with a fixed size,each 3D voxel grid cell is described by PDF to achieve rapid and accurate registration,although it depends on the good initial estimate.With the scale of point cloud map increasing,point cloud registration will inevitably produce cumulative error.Loop-closing detection is very important for reducing the accumulative error of robot trajectory and avoiding the introduction of redundant structures to ensure global consistency of maps.The methods based on the visual bag-of-words detect loop-closing can easily lead to perceptual confusion by extracting the visual features,which lead to the increase of false detection rate in loop-closing.3D point cloud map lacks high-level information to describe scenes,so it is difficult to use in robot navigation.Based on the above problems,this thesis presents a RGB-D registration method based on combinations between local visual feature and improved 3D-NDT,Firstly,FAST feature detection based on self-adaptive threshold selection is proposed to ensure enough feature points in weakly textured environment.Secondly,the fast and robust false matching points elimination algorithm is proposed by incorporating the smoothness constraints and bidirectional consistency matching.Based on the matching feature point 3D position,the initial estimate is obtained by PnP.While the distribution and number of point cloud is random in the space,the voxels based on octrees are used to subdivide point cloud by the distribution-density,then the 3D-NDT guarantee an accuracy registration.It can accurately describe the surface details of the point cloud and improve data search effectivity.In order to overcome the limitation that only depends on the visual features,a bag-of-words model which combines the visual features and geometric features of the scene is built to improve the robustness of the loop-closing algorithm in different environments.In order to make the robot understand the obstacle and the accessible areas,this thesis segments the point cloud map into two parts,and then transform them into the octree-based mapping framework OctoMap,which is essential for robot to carry out the obstacle avoidance and path planning.Experiments show that the improved point cloud registration algorithm outperforms other state of art of 3D point cloud registration methods on the adaptability to environment,precision and computation cost.Compared with the loop detection methods based on visual bag-of-words model,the algorithm can guarantee better accuracy in the case of higher recall rate.