Autonomous Mapping Robot Based On Lidar Sensor

Mapping using autonomous robots has been a hot topic in recent years for different reasons.Robot mapping consists of using a robotic system to create the cartographic representation,or a map,of an environment.This environment can have different shapes,sizes,outdoor or indoor and may be previously known or unknown.With a map created,actions such as rescuing victims from natural disaster-affected areas,most especially where humans can't reach,security,and construction,can be meticulously planned in terms of space for better efficiency and accuracy.The robotic platform designed in this industrial project is capable of autonomously navigate through previously unknown environment with no human input necessary for its operation.the mapping robot is equipped with the high resolution 360 degree scanning device LIDAR(a cheap RPLIDAR sensor)and a microprocessor or a mini computer(RASPBERRY PI 3 B PLUS)with a mapping application installed for map creation,it also performs the mapping process and localize its location relative to other objects in the environment simultaneously,The robot uses wireless technology to communicate with the computing core,eliminating the necessity of cables to exchange data.To achieve this a VNC(virtual network connection)application was introduced,in that,all the data collected by the scanner is directly transferred to the main computer for processing.the mapping process is done immediately and continuously,and this process is referred to as SLAM(simultaneous localization and mapping).SLAM approach utilizes a laser range sensor depending on a scan matching method of the successive scans.The local approach suffers from high time consumption due to iterative fashion of the scan matching method.a preceding initialization step is proposed before the local approach.This step aims to increase the convergence probability and to decrease the time consumption by limiting the number of iterations needed to reach convergence.Hector SLAM algorithm,as a global approach,suffers from getting trapped in local minima because of the employed gradient ascent.Hence,the multi-resolution map representation is utilized to avoid getting trapped in local minima.However,this approach increases the time consumption and the memory requirements of the process.but the initialization step aims to reduce the process time consumption and decrease the multiresolution map representation into a single level with small grid cell size.We also investigate the robot localization with the Monte Carlo Localization(MCL)algorithm.The goal of the algorithm is to enable a robot to localize itself in a known world.The map of the environment where the robot has to localize itself must be given to the robot beforehand.In our case the map is constructed by same robot before localization process.this map is a so-called occupancy grid.And finally,a map of the environment is created.In conclusion,to validate and evaluate the proposed algorithm,the map and localization time consumption of the proposed algorithms are compared with the MATLAB simulation,the real-world environment and the schematic representation of the environment.