Conceptual spatial representations for indoor mobile robots

Abstract

We present an approach for creating conceptual representations of human-made indoor environments using mobile robots. The concepts refer to spatial and functional properties of typical indoor environments. Following findings in cognitive psychology, our model is composed of layers representing maps at different levels of abstraction. The complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition. The system also incorporates a linguistic framework that actively supports the map acquisition process, and which is used for situated dialogue. Finally, we discuss the capabilities of the integrated system.

Autonomous construction using scarce resources in unknown environments

“…This model shares similarities with related works on semantic maps (Kuipers 2000;Zender et al 2008;Nüchter and Hertzberg 2008). However, probably because in these works robots do not manipulate the world, they do not acquire as much geometric information as our robot.…”

Modeling of a Large Structured Environment

“…Some tried to model the environment through the topology of open space in geometric map, like [6], where they employed a series of kernels for semantic labeling of regions. Some others like [5], [14] and [9] proposed spatial maps, enhanced conceptually by object recognition in regions of the map. [5] proposed a composition of two hierarchical maps, semantic and geometric anchored together.…”

“…[5] proposed a composition of two hierarchical maps, semantic and geometric anchored together. In [14] a framework of a multilayer conceptual map is developed, representing the spatial and functional properties of the environment. And [9] introduced a comprehensive framework of spatial knowledge representation for large scale semantic mapping.…”

Indoor Scene Classification Using Combined 3D and Gist Features

“…Known rooms can be recognized by invariant features in 360 • laser scans [5], by comparing current 2D features to saved views [6], and by recognizing specific objects and their configurations [7]. Burgard's group has examined a huge bunch of different approaches to the more general place categorization problem ranging from the detection of concepts like "room", "hall", and "corridor" by simple geometric features defined on a 2D laser scan [8] to subconcepts like "kitchen" [9] using ontologies encoding the relationship between objects and the subconcepts [10]. Alternatively, occurrence statistics of objects in place categories [11,12] and interdependencies among objects and locations [13] have been tried out.…”