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Explorer

Videos

Year 1

Year 2

Year 3

  • Year 3 Explorer demo (Exhaustive search; storing object location, Reacquisition, Reacquisition on retry, Failed reacquisition; revesion to exhaustive search, Exploiting serendipity: detecting objects in unexpected views) [.ogg, 8'34/57MB] [.avi, 8'34/64MB]
  • Bird's eye view of a people following run: the video visualizes the robot's internal representation of its surrounds: the robot's position with respect to a map acquired and maintained using SLAM, and the user's position extracted from laser range scans using a people tracking algorithm.

    • [video1, 0'41] In this run, the robot adapts with respect to its situation. When operating in a corridor, it adapts an "optimal-lane" following behavior, which tries to find a smooth trajectory around possible obstacles along the corridor. As a result, the robot can safely increase its top speed and also maintain a higher average speed.
    • [video2, 0'46] In this run, the robot just follows the user. It neither adapts its driving behavior on the basis of what kind of environment it is in, nor does it plan ahead to avoid obstacles. This results in a far-from-optimal motion when driving down the corridor. It is especially evident when the robot is moving past an obstacle near the end of the corridor.
  • A Discriminative Approach to Robust Visual Place Recognition [3'01]
    An important competence for a mobile robot system is the ability to localize and perform context interpretation. This is required to perform basic navigation and to facilitate local specific services. Usually localization is performed based on a purely geometric model. Through use of vision and place recognition a number of opportunities open up in terms of flexibility and association of semantics to the model.

    To achieve this the video presents an appearance based method for place recognition. The method is based on a large margin classifier in combination with a rich global image descriptor. The method is robust to variations in illumination and minor scene changes. The method is evaluated across several different cameras, changes in time-of-day and weather conditions.

Year 4

  • Multi-modal Semantic Labeling of Space [7'24]
    The problem of semantic labeling can be described as assigning meaningful semantic descriptions (e.g. "corridor" or "kitchen") to areas in the environment. Typically, semantic labeling is used as a way of augmenting the internal space representation of a robot with additional, more abstract information. This can be used by the robotic agent to enhance communication with a human user or to reason about space. The video presents a real-time experiment performed at the University of Birmingham, UK. In the experiment, the robot builds a multi-layered spatial representation with semantic place information based on multi-modal sensory input (vision and laser range data).

 

Last modified: 12.1.2009 10:07:41