2D MAPPING

2D Mapping of Robot Environment

2D Mapping of Robot Environment

Introduction

Mobile robots are characterized by their ability to move so autonomously in an unknown environment or only partially known. Their applications cover a wide range of fields, which include underground (Mining, tunneling, etc..), Underwater tasks (inspection of pipelines, measurements, search and rescue missions, etc..), space missions and exploration planetary (sample collection, maintenance of orbital stations, etc..), monitoring and safety intervention (deactivation of explosives, radioactive operation areas, etc..), military applications, and many others. In all these applications the justification more important for the application of robotics is the difficulty or impossibility of human intervention, whether direct or teleoperated. The adjective "independent" refers to the ability to perceive, model, plan and act to achieve goals without intervention, or a very little intervention from human supervisors. This marks the border between mobile robots and vehicles or machines teleoperadas, where a human operator remotely perform the above tasks. Similarly, they can be considered mobile robot, known autoguided vehicles, which are just follow preset paths (painted on the floor, on magnetic tape, etc..) or repetitive act. This is the case of edge-guided vehicles which often found in factories and industrial settings, and whose mission is to repeat over and over certain paths and actions.

2D mapping technique

Mapping tasks in the field of mobile robotics are critical to achieving autonomous or semiautonomous operation of the robot and, therefore, has been the subject of research in recent years. In this work we have implemented an algorithm to create a map of an environment and identify objects that are in el.La arrival of new measurement technologies enable the development of new algorithms for map generation and detection of objects in closed settings .

One of the fundamental skills to be have autonomous robot is the ability to build a map of the environment and navigate Porel. This concept is known as Simultaneous Loca- lization and Mapping (SLAM) and has received great interest in thelatest years [1, 2]. In the literature There difentes structure proposed for the solution of SLAM problem [3, 1]. In this paper FastSLAM algorithm used [2]. The main concept cipal of this algorithm is the existence of a con- set of particles representing the uncertainty ber of the pose of the robot. In turn, each particle estimate has an associated map in- lathe. Separate therefore the problem of SLAM into two parts: the estimation of the pose of the robot and the estimation of the map. The algorithm FastSLAM has proven to be robust to errors associated GOTIATION data and is able to represent models Nonlinear movements [5]. As to the sensors used, are pro- placed on the laser sensors are used [2, 5] or sonar [1, 2]. In this paper we have chosen the use of stereo cameras for building maps. This approach is known as SLAM vi- sual. The use of such sensors is being increasingly frequent because they are capable of get much information in- around and, moreover, are more economical than the ...