For a mobile robot to engage in exploration of a-priori unknown environments it must be able to identify locations which will yield new information when visited. This thesis presents two novel algorithms which attempt to answer the question of choosing where a robot should go next in a partially explored workspace. To begin we describe the process of acquiring highly accurate dense 3D data from a stereo camera. This approach combines techniques from a number of existing implementations and is demonstrated to be more accurate than a range of commercial offerings. Combined with state of the art visual odometry based pose estimation we can use these point clouds to drive exploration. The first exploration algorithm we present is an attempt to represent the three dimensional world as a continuous two dimensional surface. The surface is maintained as a planar graph structure in which vertices correspond to points in space as seen by the stereo camera. Edges connect vertices which have been seen as adjacent pixels in a stereo image pair, and have a weight equal to the Euclidean distance between the end points. Discontinuities in the input stereo data manifest as areas of the graph with high average edge weight, and by moving the camera to view such areas and merging the new scan with the existing graph, we push back the boundary of the explored workspace. Motivated by scaling and precision problems with the graph-based method, we present a second exploration algorithm based on continuum methods. We show that by solving Laplace’s equation over the freespace of the partially explored environment, we can guide exploration by following streamlines in the resulting vector field. Choosing appropriate boundary conditions ensures that these streamlines run parallel to obstacles and are guaranteed to lead to a frontier – a boundary between explored and unexplored space. Results are shown which demonstrate this method fully exploring three dimensional environments and outperforming oft-used information gain based approaches. We show how analysis of the potential field solution can be used to identify volumes of the workspace which have been fully explored, thus reducing future computation.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:558403 |
| Date | January 2011 |
| Creators | Shade, Robert J. |
| Contributors | Newman, Paul |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://ora.ox.ac.uk/objects/uuid:4a7d5578-f354-48e9-80b4-f3c83234be5f |
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