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Navigation Strategies for Exploring Indoor Environments Presented by Mathieu Bredif February 17, 2004 CS326A: Motion Planning.

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Presentation on theme: "Navigation Strategies for Exploring Indoor Environments Presented by Mathieu Bredif February 17, 2004 CS326A: Motion Planning."— Presentation transcript:

1 Navigation Strategies for Exploring Indoor Environments Presented by Mathieu Bredif February 17, 2004 CS326A: Motion Planning

2 Problem statement Safe and Efficient Robot Mapping Imperfect Control & Sensing SLAM: Simultaneous Localization & Mapping NBV: Next Best View Whole Navigation Sensor data Global Map Sensor Data Single Position Sensor data Updated Global Map Sensor Data Next Position

3 Visibility Region Generalization, Constraints: Line-of-sight Range Incidence Solid Curves Polylines if Workspace is Polygonal Free Curves: ≤3 parts: Line Segments Circular Arcs Log Spiral Section Safe Region: Definition nv q ∂W w

4 Safe Region: Computation Polylines fit Point cloud Ordered by θ Subset of the Visibility Region Solid lines of ∂W Free curves Safe Region Polyline approx. of the free curves

5 Next Best View Algorithm Alignment/Merging Candidate Generation Candidate Evaluation

6 NBV: Alignment / Merging Global model Local model Alignment Matches  Transformation T Needs overlap Merging Global Safe Region Global Map

7 NBV: Candidate Generation Uniform Sampling of Uniform Sampling of the visibility range of each point sample Discard the candidates that see less than overlap threshold of ALIGN. Path Planner: Discard unreachable points

8 NBV: Candidate Evaluation A(q): Area of the potentially unexplored region seen from q L(q): Length of a collision-free Path q k ->q Score = motion/expected information gain Tradeoff  NBV = argmax Sampled Points (g)

9 NBV: Algorithm Overview

10 NBV & Termination Condition Single Position Sensor data Updated Global Map L(all free curves)< Lmax Next Best View Position Global Map L(all free curves)< Lmax SLAC

11 Small Obstacles Different nature, subject to change position Detectable: narrow in-ward pointing spikes Distinct Obstacle map

12 Implementation/Example Runs

13 Short Range Example

14 Long Range Example

15 Conclusion Good Heuristic to plan the exploring Navigation Trajectory On-the-fly. (Real Time) Limited sensors: r max, tau Also limited field of view In Safe Region Navigation Warranty (if no glass doors in the workspace…)


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