Interactive Distance Field Mapping and Planning to Enable Human-Robot Collaboration

Usama Ali, Lan Wu, Adrian Mueller, Fouad Sukkar, Tobias Kaupp, Teresa Vidal Calleja

Keywords: Interactive Mapping and Planning, Euclidean Distance Fields, Gaussian Process, Human-Robot Collaboration.

TL;DR: This paper presents an interactive distance field mapping and planning (IDMP) framework aimed at dynamic scenes common in human-robot collaboration scenarios.

Abstract: Human-robot collaborative applications require scene representations that are kept up-to-date and facilitate safe motions in dynamic scenes. We present an interactive distance field mapping and planning (IDMP) framework that handles dynamic objects and collision avoidance through an efficient representation. We define interactive mapping and planning as the process of creating and updating the representation of the scene online while simultaneously planning and adapting the robot's actions based on that representation. Given depth sensor data, our framework builds a continuous field that allows to query the distance and gradient to the closest obstacle at any required position. The key aspect of this work is an efficient Gaussian Process field that performs incremental updates and implicitly handles dynamic objects with a simple and elegant formulation based on a temporary latent model.

Submission Number: 9