Topological Mapping using Visual Landmarks - PowerPoint PPT Presentation

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Topological Mapping using Visual Landmarks

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Map Updating: Not very good due to Steepest Descent algorithm. Title: Topological Mapping with Visual Landmarks Last modified by: Chi-Wei Chu Document presentation ... – PowerPoint PPT presentation

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Title: Topological Mapping using Visual Landmarks


1
Topological Mapping using Visual Landmarks
  • The work is based on the "Team Localization A
    Maximum Likelihood Approach" paper.
  • To simplify the problem, assume
  • Landmarks are directional (In Player-Stage
    simulation, landmarks are represented as pairs of
    color boxes.)
  • Each episode of motion consists of an in-place
    rotation by ?. Followed by moving straight
    forward by d. ? and d are corrupted by zero-mean
    Gaussian noises.
  • The motion and observation forms a directed
    graph.
  • When a cycle was detected, use maximum
    likelihood to update graph nodes.

2
Problem One Cycle Detection
  • Unlike team localization, in which each Robot can
    be identified correctly, Landmarks with the same
    visual cues (color pairs) are indistinguishable.
  • Cycle Detection, when observing a landmark the
    robot has seen before
  • Backup old map.
  • Assume a cycle is detected, update map
    accordingly.
  • After updating the map, compute the average
    negative-log-likelihood of updated graph edges.
    If the average is below a threshold, keep the new
    map.
  • Otherwise, restore the old map and add a new
    landmark.
  • The problem is setting the threshold, which
    depends on the motion model parameters.

3
Problem Two Gradient Descent
  • The project uses the simplest Steepest Descent
    algorithm to update the map. Which poses the
    problem of slow converging.
  • Each node in the graph update itself to maximize
    the total local likelihood of connecting edges.
    The global maximum then is slow to reach.
  • Exploiting the algorithm
  • When update motion nodes, ignore landmark nodes
    that is newly observed and have not been updated
    before, except the on which forms the cycle.
  • Applying better gradient descent algorithmmay
    improve the result.

4
Result
  • Cycle Detection
  • Can detect cycles when observed a landmark the
    robot have seen before.
  • Can distinguish similar landmarks that are placed
    not too close to each other.
  • Map Updating
  • Not very good due to Steepest Descent algorithm.
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