Abstract
An autonomous robot performing tasks in a human environment needs to recognize semantic information about places. Semantic mapping is a task in which suitable semantic information is assigned to an environmental map so that a robot can communicate with people and appropriately perform tasks requested by its users. We propose a novel statistical semantic mapping method called SpCoMapping, which integrates probabilistic spatial concept acquisition based on multimodal sensor information and a Markov random field applied for learning the arbitrary shape of a place on a map.SpCoMapping can connect multiple words to a place in a semantic mapping process using user utterances without pre-setting the list of place names. We also develop a nonparametric Bayesian extension of SpCoMapping that can automatically estimate an adequate number of categories. In the experiment in the simulation environments, we showed that the proposed method generated better semantic maps than previous semantic mapping methods; our semantic maps have categories and shapes similar to the ground truth provided by the user. In addition, we showed that SpCoMapping could generate appropriate semantic maps in a real-world environment.
Highlights
An autonomous robot performing tasks in our daily environment needs to recognize semantic information regarding the place
SpCoMapping has a higher average, showing a higher performance on each map, compared to the other methods. This result suggests that SpCoMapping can solve the problems introduced in section 1, including the overwrite and shape problems; in other words, the categories of semantic maps it generates are closer than the other semantic mapping methods to the categories of semantic maps generated by a person
This paper proposed a novel semantic mapping method called SpCoMapping extended a spatial concept acquisition method using Markov random field (MRF)
Summary
An autonomous robot performing tasks in our daily environment needs to recognize semantic information regarding the place. When an autonomous vacuum cleaner robot tries to understand a command given by its user, e.g., “clean Joseph’s room,” the robot needs to be able to locate “Joseph’s room” on its map of the environment in order to clean that place. Semantic mapping is the task through which suitable semantic information is assigned to a robot’s map so that it can communicate with people and appropriately perform tasks requested by its users (Kostavelis and Gasteratos, 2015). Many previous studies on semantic mapping (Kostavelis and Gasteratos, 2015; Goeddel and Olson, 2016; Sünderhauf et al, 2016; Himstedt and Maehle, 2017; Brucker et al, 2018; Posada et al, 2018; Rangel et al, 2019) have
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