A single sensor system for mapping in GNSS-denied environments. (August 2019)

• Record Type: Journal Article
• Authors: de Paula Veronese, Lucas; Badue, Claudine; Auat Cheein, Fernando; Guivant, Jose; De Souza, Alberto Ferreira
• Is Part Of: Cognitive systems research. Volume 56(2019)
• Page Start: 246
• Page End: 261
• Publication Date: 2019-08
• Subjects: Robotics -- Autonomous vehicles -- SLAM -- GraphSLAM -- Mapping; Cognition -- Periodicals; Cognitive engineering (System design) -- Periodicals; Artificial intelligence -- Periodicals; 153.05
• DOI: 10.1016/j.cogsys.2019.03.018
• Languages: English
• ISSNs: 1389-0417

Abstract

Abstract: Autonomous robotic vehicles need accurate positioning to navigate. For outdoor autonomous vehicles, the localization problem has been solved using GNSS systems. However, many places suffer from problems in the signal of those systems, known as GNSS-denied environments. To face such a problem, several approaches first map the environment to thereafter localize the vehicle within it. The solutions for mapping in GNSS-denied places, mostly, are tested indoors, and the revisited areas are generally close from the starting position. In this work, we develop a single sensor system for mapping in large-scale GNSS-denied sites, based exclusively on a 3D LiDAR system. The proposed work consists of a 3D-LiDAR with a LiDAR Odometry approach (LO) estimating movements between frames thus providing of dead-reckoning estimates of the vehicle. The LiDAR Odometry is the input to a virtual GNSS system based on a Particle Filter Localization which matches the estimated dead-reckoning with a road map, with the assumption that the vehicle usually navigates on roads. The global position produced by the virtual GNSS is used to detect and correct loop closures in case of revisited areas. A GraphSLAM implementation fuses the outcomes from LiDAR Odometry, virtual GNSS and loop closure, and yield a feasible pose of the vehicle. Finally, a mapping procedure places every 3D frame and builds 2D occupancy grid maps. The system developed here is evaluated empirically using two datasets collected in a dynamic environment with paths of 3.7 and 6.5 km long, respectively. For both datasets, the system presents an RMS of 6.5 m according to GNSS sensor readings used for comparison purposes.