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Human-centered environments are rich with a wide variety of spatial relations between everyday objects. For autonomous robots to operate effectively in such environments, they should be able to reason about these relations and generalize them to objects with different shapes and sizes. For example, having learned to place a toy inside a basket, a robot should be able to generalize this concept using...
In autonomous exploration tasks, robots usually rely on a SLAM system to build a map of the environment online and then use it for navigation purposes. Although there has been substantial progress in robustly building accurate maps, these systems cannot guarantee the consistency of the resulting environment model. In this paper, we address the problem of robustly guiding a robot back to its starting...
In the last years, many researchers started to consider teach-and-repeat approaches for reliable autonomous navigation. The paradigm, in all its proposed forms, is deeply rooted in the idea that the robot should autonomously follow a route that has been demonstrated by a human during a teach phase. However, human demonstrations are often inefficient in terms of execution time or may cause premature...
Map consistency is an important requirement for applications in which mobile robots need to effectively perform autonomous navigation tasks. While recent SLAM techniques provide an increased robustness even in the context of bad initializations or data association outliers, the question of how to determine whether or not the resulting map is consistent is still an open problem. In this paper, we introduce...
Over the years, proposals have been made to employ line segments to build 2D maps of indoor environments. One of the basic steps of these approaches is the matching between scans (or, more generally, sets) of line segments, which is usually addressed using variants of the Iterative Closest Line (ICL) paradigm. ICL is based on the idea of associating closest line segments belonging to the two scans...
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