Scene object recognition for mobile robots through Semantic Knowledge and Probabilistic Graphical Models

Online context-based object recognition for mobile robots

“…There are also examples relying on Conditional Random Fields (CRFs), like [6], to classify objects into four categories: wall, floor, ceiling and clutter, and the work presented in [7], where the faces of a triangular mesh representing the scene are assigned to object classes. CRFs are also used in [8], [9], and [10], in conjunction with common-sense information codified into an ontology, for the recognition of objects appearing in office and domestic scenes, and in [4] for the modeling of context in RGB images. However, despite of the effort that has been made for properly modeling and exploiting objects' contextual information, less attention has been paid to their applicability to different mobile robot tasks [16], which probably imposes computational and/or time execution constrains.…”

“…Regarding the exploitation of contextual information, one-shot systems are seriously limited by the sensor frustum and possible occlusions, given that they are able to observe only a portion of the objects and relations appearing in the inspected scene. Some approaches cope with this issue by registering a number of observations prior to the recognition process in order to obtain a wider view of the scene [5][6][7][8][9][10]. However, the time and computational resources needed for gathering and registering such observations prevents their use in most robotic applications.…”

“…A large, growing body of literature has resorted to the Probabilistic Graphical Models (PGMs) framework [2] for modeling contextual relations [3][4][5][6][7][8][9][10]. In this framework, a set of weights are learned in a supervised training process [11], and then exploited by probabilistic inference to categorize and recognize sensory data.…”

“…Most works address the problem through one-shot recognition systems [3], [4], [9], [10], which recognize objects relying on single observations of the scene (in the form of RGB, depth or RGB-D images). Regarding the exploitation of contextual information, one-shot systems are seriously limited by the sensor frustum and possible occlusions, given that they are able to observe only a portion of the objects and relations appearing in the inspected scene.…”

OLT: A Toolkit for Object Labeling applied to robotic RGB-D datasets

“…Nowadays, given the increasing number of capabilities and applications that are demanded to a mobile robot, e.g. semantic mapping [4], high-level decision making [5], or contextual object recognition [6]- [9], new particularly oriented datasets are required.…”