The goal of simultaneous localization and mapping (SLAM) is to compute the posterior distribution over landmark poses. Typically, this is made possible through the static world assumption - the landmarks remain in the same ...

Service robots can benefit from maps that support their tasks and facilitate communication with humans. For efficient interaction, it is practical to be able to reference structures and objects in the environment, e.g. ...

This paper will explore the relationship between sensory accuracy and Simultaneous Localization and Mapping (SLAM) performance. As inexpensive robots are developed with commodity components, the relationship between ...

Object discovery and modeling have been widely studied in the computer vision and robotics communities. SLAM approaches that make use of objects and higher level features have also recently ...

Semantic mapping aims to create maps that include meaningful features, both to robots and humans. We present an extension to our feature based mapping technique that includes information about the locations of horizontal ...

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 ...

Complex and structured landmarks like objects have many advantages over low-level image features for semantic mapping. Low level features such as image corners suffer from occlusion boundaries, ambiguous data association, ...

We present a mobile manipulation system used by the Georgia Tech team in the RoboCup@Home 2010 competition. An overview of the system is provided, including the approach taken for manipulation, SLAM, object detection, ...

Simultaneous Localization and Mapping (SLAM) aims to estimate the maximum likelihood map and robot pose based on a robot’s control and sensor measurements. In structured environments, such as human environments, we might ...