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Constraint-free Topological Mapping and Path Planning by Maxima Detection of the Kernel Spatial Clearance Density

Abstract : Asserting the inherent topology of the environment perceived by a robot is a key prerequisite of high-level decision making. This is achieved through the construction of a concise representation of the environment that endows a robot with the ability to operate in a coarse-to-fine strategy. In this paper, we propose a novel topological segmentation method of generic metric maps operating concurrently as a path-planning algorithm. First, we apply a Gaussian Distance Transform on the map that weighs points belonging to free space according to the proximity of the surrounding free area in a noise resilient mode. We define a region as the set of all the points that locally converge to a common point of maximum space clearance and employ a weighed meanshift gradient ascent onto the kernel space clearance density in order to detect the maxima that characterize the regions. The spatial intra-connectivity of each cluster is ensured by allowing only for linearly unobstructed mean-shifts which in parallel serves as a path-planning algorithm by concatenating the consecutive mean-shift vectors of the convergence paths. Experiments on structured and unstructured environments demonstrate the effectiveness and potential of the proposed approach.
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https://hal.inria.fr/hal-00759003
Contributor : Panagiotis Papadakis <>
Submitted on : Thursday, November 29, 2012 - 5:03:25 PM
Last modification on : Wednesday, September 26, 2018 - 4:32:03 PM
Long-term archiving on: : Saturday, December 17, 2016 - 5:04:16 PM

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Panagiotis Papadakis, Mario Gianni, Matia Pizzoli, Fiora Pirri. Constraint-free Topological Mapping and Path Planning by Maxima Detection of the Kernel Spatial Clearance Density. International Conference on Pattern Recognition Application and Methods, 2012, Vilamoura, Portugal. ⟨10.5220/0003735300710079⟩. ⟨hal-00759003⟩

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