Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 46, Issue 5, Pages 1217-1228Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2015.2430526
Keywords
Mobile robots; navigation; path planning
Categories
Funding
- Hong Kong University of Science and Technology [IGN13EG03]
- General Research Fund by Research Grants Council Hong Kong [16206014]
- National Natural Science Foundation of China [6140021318]
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This paper deals with the path-planning problem for mobile wheeled-or tracked-robot which drive in 2.5-D environments, where the traversable surface is usually considered as a 2-D-manifold embedded in a 3-D ambient space. Specially, we aim at solving the 2.5-D navigation problem using raw point cloud as input. The proposed method is independent of traditional surface parametrization or reconstruction methods, such as a meshing process, which generally has high-computational complexity. Instead, we utilize the output of 3-D tensor voting framework on the raw point clouds. The computation of tensor voting is accelerated by optimized implementation on graphics computation unit. Based on the tensor voting results, a novel local Riemannian metric is defined using the saliency components, which helps the modeling of the latent traversable surface. Using the proposed metric, we prove that the geodesic in the 3-D tensor space leads to rational path-planning results by experiments. Compared to traditional methods, the results reveal the advantages of the proposed method in terms of smoothing the robot maneuver while considering the minimum travel distance.
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