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Probabilistic Contextual Situation Analysis

  • Guy Ramel
  • Roland Siegwart
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 46)

Abstract

Mobile robots are gradually appearing in our daily environment. To navigate autonomously in real-world environments and interact with objects and humans, robots face various major technological challenges. Among the required key competencies of such robots is their ability to perceive the environment and reason about it, to plan appropriate actions. However, sensory information perceived from real-world situations is error prone and incomplete and thus often results in ambiguous interpretations. We propose a new approach for object recognition that incorporates visual and range information with spatial arrangement between objects (context information). It is based on using Bayesian networks to fuse and infer information from different data.In the proposed framework, we first extract potential objects from the scene image using simple features- characteristics like colour or the relation between height and width. This basic information is easy to extract but often results in ambiguous situations between similar objects. To resolve ambiguities among the detected objects, the relative spatial arrangement (context information) of the objects is used in a second step. Consider, for example, a cola can and a red trash can that are both cylindrical, have similar ratios between width and height and have very similar colours. Depending on their distances from the robot, they may be hard to distinguish. However, if we further consider their spatial arrangement with other objects, e.g. a table, they might be clearly differentiable, the cola can typically standing on the table and the trash can on the floor. This contextual information is therefore a very efficient way to increase drastically the reliability of object recognition and scene interpretation. Moreover, range information from a laser scanner and speech recognition offer complementary information to improve reliability further. Thus, an approach using laser range data to recognize places (such as corridors, crossings, rooms and doors) using Bayesian programming is also developed for both topological navigation in a typical indoor environment and object recognition.

Keywords

Mobile Robot Bayesian Network Context Information Gesture Recognition Reference Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Guy Ramel
    • 1
  • Roland Siegwart
    • 2
  1. 1.Autonomous Systems Lab, Ecole Polytechnique Fédérale de Lausanne  
  2. 2.Autonomous Systems Lab, Institute of Robotics and Intelligent SystemsETH Zürich 

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