CoMapping: Multi-robot Sharing and Generation of 3D-Maps applied to rural and urban scenarios

International audienceWe present an experimental study for the generation of large 3D maps using our CoMapping framework. This framework considers a collaborative approach to efficiently manage, share, and merge maps between vehicles. The main objective of this work is to perform a cooperative mapping for urban and rural environments denied of continuous-GPS service. The study is split in to 2 stages: Pre-Local and Local. In the first stage, each vehicle builds a Pre-Local map of its surroundings in real-time using laser-based measurements, then relocates the map in a global coordinate system using just the low cost GPS data from the first instant of the map construction. In the second stage, vehicles share their pre-local maps, align and merge them in a decentralized way in order to generate more consistent and larger maps, named Local maps. To evaluate performance of all the cooperative system in terms of map alignments, tests are conducted using 3 cars equipped with LiDARs and GPS receiver devices in urban outdoor scenarios of thé Ecole Centrale Nantes campus and rural environments

CoMapping: Efficient 3D-Map Sharing Methodology for Decentralized cases

International audienceCoMapping is a framework to efficient manage, share, and merge 3D map data between mobile robots. The main objective of this framework is to implement a Collaborative Mapping for outdoor environments. The framework structure is based on two stages. During the first one, the Pre-Local Mapping stage, each robot constructs a real time pre-local map of its environment using Laser Rangefinder data and low cost GPS information only in certain situations. Afterwards, the second one is the Local Mapping stage where the robots share their pre-local maps and merge them in a decentralized way in order to improve their new maps, renamed now as local maps. An experimental study for the case of decentralized cooperative 3D mapping is presented, where tests were conducted using three intelligent cars equipped with LiDAR and GPS receiver devices in urban outdoor scenarios. We also discuss the performance of the cooperative system in terms of map alignments