Social Mapping of Human-Populated Environments by Implicit Function Learning

International audienceWith robots technology shifting towards entering human populated environments, the need for augmented perceptual and planning robotic skills emerges that complement to human presence. In this integration, perception and adaptation to the implicit human social conventions plays a fundamental role. Toward this goal, we propose a novel framework that can model context-dependent human spatial interactions, encoded in the form of a social map. The core idea of our approach resides in modelling human personal spaces as non-linearly scaled probability functions within the robotic state space and devise the structure and shape of a social map by solving a learning problem in kernel space. The social borders are subsequently obtained as isocontours of the learned implicit function that can realistically model arbitrarily complex social interactions of varying shape and size. We present our experiments using a rich dataset of human interactions, demonstrating the feasibility and utility of the proposed approach and promoting its application to social mapping of human-populated environments

Constraint-free topological mapping and path planning by maxima detection of the kernel spatial clearance density

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