We are given spans of the target text which align to concepts in the AMR graph.These alignment do not cover every token in the target sentnce. Typically function words are not aligned to any graph fragment. Next, we obtain word alignments between the target sentence and source sentence. Since we have word alignments between target and source, and phrase alignments between target and AMR graph, we must convert the word alingments into phrase alignments. The phrases on the source side will then be projected to the AMR concepts via the target sentence
This paper implements Simultaneous Localization and Mapping (SLAM) technique to construct a map of a given environment. A Real Time Appearance Based Mapping (RTAB-Map) approach was taken for accomplishing this task. Initially, a 2d occupancy grid and 3d octomap was created from a provided simulated environment. Next, a personal simulated environment was created for mapping as well. In this appearance based method, a process called Loop Closure is used to determine whether a robot has seen a location before or not. In this paper, it is seen that RTAB-Map is optimized for large scale and long term SLAM by using multiple strategies to allow for loop closure to be done in real time and the results depict that it can be an excellent solution for SLAM to develop robots that can map an environment in both 2d and 3d.