Active Shape Completion Using Tactile Glances

摘要

One longstanding challenge in the field of robotics has been the robust and reliable grasping of objects of unknown shape. Part of that challenge lies in reconstructing the object's shape using only limited observations. Most approaches use either visual or tactile information to reconstruct the shape, having to face issues resulting from the limitations of the chosen modality. This thesis tries to combine the strengths of visual and tactile observations by taking the result from an existing visual approach and refining that result through sparse tactile glances. The existing approach produces potential shape hypotheses in voxel space which get combined into one final shape. This thesis takes that final shape and determines voxels of interest using either entropy or variance. These voxels will be targeted by the exploration, providing information about these voxels. This information will be used to assign weights to the original hypotheses in order for the combined shape to better fit the observations. All explorations are simulated and evaluated in MATLAB. The resulting shapes are evaluated based on their Jaccard Index with the ground truth model. The algorithm leads to improvements in the Jaccard Index, but not to drastically different looking shapes.