1,721,010 research outputs found
Toward generalization of experimental results for autonomous robots
No full textIn this paper we discuss some issues in the experimental evaluation of intelligent autonomous systems,
focusing on systems, like autonomous robots, operating in physical environments. We argue that one
of the weaknesses of current experimental practices is the low degree of generalization of experimental
results, meaning that knowing the performance a robot system obtains in a test setting does not provide
much information about the performance the same system could achieve in other settings. We claim that
one of the main obstacles to achieve generalization of experimental results in autonomous robotics is the
low degree of representativeness of the selected experimental settings. We survey and discuss the degree
of representativeness of experimental settings used in a significant sample of current research and we
propose some strategies to overcome the emerging limitations
A System for Building Semantic Maps of Indoor Environments Exploiting the Concept of Building Typology
No full textOnline switch of communication modalities for efficient multirobot exploration
No full textExploration of unknown environments with multirobot systems subject to communication constraints is a task involved in several applications, like search and rescue and monitoring. The approaches proposed so far to address this problem are usually based on the use of a single communication modality, for instance, either multi-hop (MH) or rendezvous (RV), during the whole mission. However, it has been conjectured that online switching between communication modalities could be beneficial. In this work, we empirically investigate this hypothesis by presenting an exploring multirobot system that can originally switch between communication modalities during an exploration mission
Exploiting Structural Properties of Buildings Towards General Semantic Mapping Systems
No full textSemantic mapping is one of the most active and promising research areas within autonomous mobile robotics. Informally, a semantic map associates a high-level human-understandable label (like “office” or “corridor”) to a portion of an environment. Most semantic mapping approaches are based on classifiers that, given some features perceived by robot sensors in a physical place, associate a semantic label to the place. These approaches are often tested on a limited number of homogeneous places (e.g., few rooms within a single building). This line of action seems to hinder the development of methods for constructing semantic maps that can be (re)used in a number of previously unseen environments. In this paper, we aim at contributing to make semantic mapping methods more general. In particular, we focus on indoor environments and we consider the following research question: to what extent are the semantic mapping approaches shown to label rooms in a single building expected to work when applied to different buildings
A generative spectral model for semantic mapping of buildings
No full textConsider a mobile robot exploring an initially unknown school building and assume that it has already discovered some classrooms, offices, and bathrooms. What can the robot infer about the presence and the locations of other classrooms and offices in the school building? This paper makes a step toward providing an answer to the above question by proposing a system based on a generative model that is able to represent the topological structures and the semantic labeling schemas of buildings and to predict the structure and the schema for unexplored portions of these environments. We represent the buildings as undirected graphs, whose nodes are rooms and edges are physical connections between them. Given an initial knowledge base of graphs, our approach, relying on a spectral analysis of these graphs, segments each graph for finding significant subgraphs and clusters them according to their similarity. A graph representing a new building or an unvisited part of a building is eventually generated by sampling subgraphs from clusters and connecting them
Semantic classification by reasoning on the whole structure of buildings using statistical relational learning techniques
No full textSemantic mapping for autonomous mobile robots includes the place classification task that associates semantic labels (like 'corridor' or 'office') to rooms perceived in indoor environments. The mainstream approaches to place classification are characterized by local reasoning, where only features relative to the neighbourhood of each room are considered. In this paper, we propose a method for global reasoning on the whole structure of buildings, considered as single structured objects. We use a statistical relational learning algorithm, called kLog, and we compare it against a classifier, Extra-Trees, which resembles classical local approaches, in three tasks: classification of rooms, classification of entire floors of buildings, and validation of simulated worlds. Our results show that our global approach performs better than local approaches when the classification task involves reasoning on the regularities of buildings and when available information about rooms is coarse-grained
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