Semantic map partitioning in indoor environments using regional analysis

Abstract

Classification of spatial regions based on semantic information in an indoor environment enables robot tasks such as navigation or mobile manipulation to be spatially aware. The availability of contextual information can significantly simplify operation of a mobile platform. We present methods for 3utomHted recognition and classification of SI)aceS into separHte semantic regions and use of sueh information for generHtion of a topological map of an environment. The association of semantic labels with spatial regions is based on Humal/ Augmented Mappil/g. T he methods presented in this paper are evalu ated both in simu lation and on real data acquired from an office environment.

DEDUCE: Diverse scEne Detection methods in Unseen Challenging Environments

“…The measurements of simple objects like points, lines, and planes are data associated with mapped landmarks using the joint compatibility branch and bound (JCBB) technique [41]. The regions for color segmentation are acquired by the Gaussian Region algorithm of [42]. However, in [42], the map partitions were built through human guidance, whereby the robot was taken on a tour of the space (either by driving the robot manually, or using a person following behavior) and the respective scene labels were taught to it.…”

“…The regions for color segmentation are acquired by the Gaussian Region algorithm of [42]. However, in [42], the map partitions were built through human guidance, whereby the robot was taken on a tour of the space (either by driving the robot manually, or using a person following behavior) and the respective scene labels were taught to it. This is in contrast to our approach, where the labels are learned from our visual place categorization system.…”

Semantic labeling for indoor topological mapping using a wearable catadioptric system

“…Regarding how to learn the environment model, some proposals are constantly trained and, sometimes, simultaneously run with human supervision to achieve a representation closer to human concepts [4,27]. Others use weaker human supervision to learn a model from a few initial labeled samples, such as the work in [9].…”

On-line semantic mapping

“…In [12] a contextual topological map is built through the interaction with the user. In [13] a multivariate probabilistic model is used to associate a spatial region to a semantic label, while a user guide supports the robot in this process, by instructing it in selecting the labels.…”