Advancing Simultaneous Localization and Mapping (SLAM) for Robots in Unstructured Terrain

Author(s)	Priyanka Das
Country	USA
Abstract	Simultaneous Localization and Mapping (SLAM) has revolutionized the field of robotics, making robots more effective and useable in unstructured environments. Its focus is to enable a robot to continuously gather important information regarding its position in real-time by reconstructing the map around the robot and finding the robot’s location inside that map through sensors, cameras, and laser range finders. This paper discusses SLAM technology and details different techniques to enhance SLAM in unstructured terrain. SLAM operates with the help of various key components essential to its positioning and navigation, including sensors, mapping, and localization. Approaches to enhancing SLAM in unstructured are based on optimizing cameras, known as visual SLAM, while others are techniques in LIDAR sensor choices. Adding an RGB-D camera increases the reliability as monocular and binocular cameras may give incorrect geometrical information. Another technique to advance SLAM is the deep learning method, which involves continuous learning in a robot’s environment to increase its accuracy and effectiveness.
Keywords	Navigation, Robots, Sensors, Simultaneous Localization and Mapping (SLAM), Unstructured environments, Unstructured Terrain
Published In	Volume 2, Issue 6, November-December 2020
Published On	2020-12-22
DOI	https://doi.org/10.36948/ijfmr.2020.v02i06.25432
Short DOI	https://doi.org/g8zmgs

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International Journal For Multidisciplinary Research