3D lidar SLAM-based systems in object detection and navigation applications

Abstract

ABSTRACTThis paper considered an object detection system based on 3D LiDAR Sensor and Simultaneous Localization and Mapping (SLAM) to complete the navigation applications of mobile robots. A 3D-based SLAM with lightweight and ground-optimized Lidar odometry and mapping (LeGO-LOAM) appropriately generated the environmental maps. SLAM is a tool used to obtain information from the environment, allowing mobile robots to know their location. Indoor environment data is immedicably created while SLAM is processing the information. The dynamic object detection algorithm depends on the available information to realize the external morphology and circle the bounding box of moving objects. Therefore, a wheeled mobile robot (WMR) was employed to dynamically trace the object’s movement direction. Finally, This study found that the quantum genetic algorithm (QGA) is more efficient in generating a shorter path than the particle swarm optimization, and a dynamic window approach (DWA) is immediately detected as a dynamic obstacle. Therefore, WMR obtains enough object, obstacle, and routing information to effectively and safely reach the destination through the Move_base software package in Robot Operating System.CO EDITOR-IN-CHIEF: Kuo, Cheng-ChienASSOCIATE EDITOR: Zhang, XuefengKEYWORDS: Wheeled mobile robot (WMR)simultaneous localization and mapping (SLAM)navigationobject detection Nomenclature c=roughness degree.cth=Threshold of roughness degree.Fet=Current edge features.Fpt=Current planner feature.Fet−1=Previous edge features.Fpt−1=Previous planner feature.Mt−1=Previous set of all feature setspi=a point in Pt.Pt=the obtained frame of point cloud information.Qt−1=Previous point cloud map.ri=A distance between pi and the sensor.rj=A distance between pj and the sensor.tx=x coordinate of the robot at time tty=y coordinate of the robot at time ttz=z coordinate of the robot at time tθpitch=the pitch angle of the robot at time tθroll=the roll angle of the robot at time tθyaw=the yaw angle of the robot at time tAcknowledgmentsThis paper was supported by the Ministry of Science and Technology (MOST) of the Republic of China under contract MOST 109-2221-E-507-009, MOST 109-2221-E-032-038, and MOST 109-2221-E-032-036.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThe work was supported by the Ministry of Science and Technology (MOST) [109-2221-E-032-036].