WeCo-SLAM: Wearable Cooperative SLAM System for Real-time Indoor Localization Under Challenging Conditions

Viachaslau Kachurka (1) , Bastien Rault (2) , Fernando Israel Ireta Muñoz (3) , David Roussel (1) , Fabien Bonardi (1) , Jean-Yves Didier (1) , Hicham Hadj-Abdelkader (1) , Samia Bouchafa (1) , Pierre Alliez (3) , Maxime Robin (2)

1 IBISC - Informatique, BioInformatique, Systèmes Complexes
2 INNODURA TB
3 TITANE - Geometric Modeling of 3D Environments

Real-time globally consistent GPS tracking is critical for an accurate localization and is crucial for applications such as autonomous navigation or multi-robot mapping. However, under challenging environment conditions such as indoor/outdoor transitions, GPS signals are partially available or not consistent over time. In this paper, a real-time tracking system for continuously locating emergency response agents in challenging conditions is presented. A cooperative localization method based on Laser-Visual-Inertial (LVI) and GPS sensors is achieved by communicating optimization events between a LiDAR-Inertial-SLAM (LI-SLAM) and Visual-Inertial-SLAM (VI-SLAM) that operate simultaneously. The estimation of the pose assisted by multiple SLAM approaches provides the GPS localization of the agent when a stand-alone GPS fails. The system has been tested under the terms of the MALIN Challenge, which aims to globally localize agents across outdoor and indoor environments under challenging conditions (such as smoked rooms, stairs, indoor/outdoor transitions, repetitive patterns, extreme lighting changes) where it is well known that a stand-alone SLAM will not be enough to maintaining the localization. The system achieved Absolute Trajectory Error of 0.48%, with a pose update rate between 15 and 20 Hz. Furthermore, the system is able to build a global consistent 3D LiDAR Map that is post-processed to create a 3D reconstruction at different level of details.

Mots clés: Computer vision; Simultaneous localization and mapping; Sensor fusion; Indoor navigation; Embedded software; Terrain mapping

Domaines: Automatique / Robotique; Traitement du signal et de l'image [eess.SP]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Viachaslau Kachurka, Bastien Rault, Fernando Israel Ireta Muñoz, David Roussel, Fabien Bonardi, et al.. WeCo-SLAM: Wearable Cooperative SLAM System for Real-time Indoor Localization Under Challenging Conditions. IEEE Sensors Journal, 2022, 22 (6), pp.5122--5132. ⟨10.1109/JSEN.2021.3101121⟩. ⟨hal-03609471⟩