This paper introduces a Wearable SLAM system that performs indoor and outdoor SLAM in real time. The related project is part of the MALIN challenge which aims at creating a system to track emergency response agents in complex scenarios (such as dark environments, smoked rooms, repetitive patterns, building floor transitions and doorway crossing problems), where GPS technology is insufficient or inoperative. The proposed system fuses different SLAM technologies to compensate the lack of robustness of each, while estimating the pose individually. LiDAR and visual SLAM are fused with an inertial sensor in such a way that the system is able to maintain GPS coordinates that are sent via radio to a ground station, for real-time tracking. More specifically, LiDAR and monocular vision technologies are tested in dynamic scenarios where the main advantages of each have been evaluated and compared. Finally, 3D reconstruction up to three levels of details is performed.

Real-time globally consistent GPS tracking is critical for an accurate localization and is crucial for applica-tions 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 local-ization 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.

This paper presents a use case for SLAM techniques applied to real time localization and detailed mapping for emergency response personnel in non cooperative environments. Such environments tend to defeat conventional localization approaches, therefore we must ensure continuous operation of our localization and mapping regardless of the difficulties encountered (lack of GPS signals, lighting conditions, smoke, etc.). The proposed system fuses two SLAM algorithms, a LiDAR-based and a camera-based. Since LiDAR-based SLAM uses dense 3D measurements, it is well suited to the construction of a detailed map, while the visual SLAM allows to quickly recognize already visited places in order to apply loop closure corrections, by using a key frames graph. The currently proposed system allows collaboration between these two SLAMs through pose sharing and relocalization.

This paper deals with recent advances in acoustic experimental methods and especially acoustic imaging. The paper covers two areas of interest to acousticians. In the first part, it is explained how near-field acoustic holography (NAH) can be extended with beamforming in the near-field, focalization. The combination of the two methods is providing now a source localization solution with a good spatial resolution over the complete frequency range without the burden of measuring a large number of points as would be required if only NAH was used. In the second part of the paper, a method is described that goes one step beyond source localization. It is interesting to locate the noise sources, but from an engineering standpoint it is even more interesting to know the internal sources/forces causing these noise sources. In this part is explained how source localization techniques in conjunction with artificial excitation of the structure can provide information on the internal sources of the structure.

Europe has a growing aging population, leading to the need for adapted healthcare services. Our work aims at proposing a solution for falls detection of elderly people using sound recognition based on a hierarchical i-vectors system. The system presented in this paper improves significantly the accuracy of sound recognition compared to the state of the art methods. The latter provides a good recognition rate of 81.98% on noiseless sounds. This system needs to be tested in a noisy environment and this can be improved by using new sound descriptors.