Indoor Localization and Mapping: Towards Tracking Resilience Through a Multi-SLAM Approach

Alliez, Pierre; Bonardi, Fabien; Bouchafa, Samia; Didier, Jean‐Yves; Hadj-Abdelkader, Hicham; Munoz, Fernando Ireta; Kachurka, Viachaslau; Rault, Bastien; Robin, Maxime; Roussel, David

doi:10.1109/med48518.2020.9182794

Cited by 6 publications

(6 citation statements)

References 18 publications

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“…The performance is reliable when the robot moves for a long distance, but it is not suitable for situations where the movement speed is too fast. P. Alliez et al [170] developed a SLAM system equipped with dual LIDAR, an IMU, a GPS receiver, and camera sensors for emergencies in the military and civilian fields. The information of each sensor is fused using the loose coupling method.…”

Section: Fusion Methods Based On Uncertaintymentioning

confidence: 99%

SLAM Overview: From Single Sensor to Heterogeneous Fusion

et al. 2022

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After decades of development, LIDAR and visual SLAM technology has relatively matured and been widely used in the military and civil fields. SLAM technology enables the mobile robot to have the abilities of autonomous positioning and mapping, which allows the robot to move in indoor and outdoor scenes where GPS signals are scarce. However, SLAM technology relying only on a single sensor has its limitations. For example, LIDAR SLAM is not suitable for scenes with highly dynamic or sparse features, and visual SLAM has poor robustness in low-texture or dark scenes. However, through the fusion of the two technologies, they have great potential to learn from each other. Therefore, this paper predicts that SLAM technology combining LIDAR and visual sensors, as well as various other sensors, will be the mainstream direction in the future. This paper reviews the development history of SLAM technology, deeply analyzes the hardware information of LIDAR and cameras, and presents some classical open source algorithms and datasets. According to the algorithm adopted by the fusion sensor, the traditional multi-sensor fusion methods based on uncertainty, features, and novel deep learning are introduced in detail. The excellent performance of the multi-sensor fusion method in complex scenes is summarized, and the future development of multi-sensor fusion method is prospected.

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Section: Fusion Methods Based On Uncertaintymentioning

confidence: 99%

SLAM Overview: From Single Sensor to Heterogeneous Fusion

et al. 2022

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“…The Zena dataset was proposed for outdoor localisation in [19] to further research on Search & Rescue scenarios. Camera and Lidar vision were fused in [2] using a Multi-SLAM approach to support localisation of pedestrians using a shoulder mounted Velodyne VLP-16. To understand the behavioural changes, a portable Lidar apparatus with localisation capability was worn by a pedestrian and used to observe the long-term and wide-area behaviour of pedestrians in [11].…”

Section: Body Worn Sensorsmentioning

confidence: 99%

Living with Rules: An AR Approach

Kamalasanan

Sester

2020

2020 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)

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The Social distancing rule has proven to be an effective measure against the spread of the infectious COronaVIrus Disease 2019 (COVID-19). Even with a lot of research focusing on static camera based solutions for monitoring the rule, the real issue with visualising and monitoring rules for public spaces still remain an open question. In this work we propose a Social Distancing Helmet (SDH) with basic prototyping for an outdoor augmentation system using body worn sensors for visualising and monitoring rules for shared spaces using AR. First results with some software components of the prototype are presented.

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“…This strategy allows the system to maintain a valid trajectory under arbitrary and unpredictable challenging conditions such as smoke-filled or dark rooms, indoor/outdoor transitions, extreme lighting conditions or even agent-crawling. Our system is an extension of previous works [9], [10] that have been proposed and tested under the environment constraints of the MALIN Challenge [11], which aims to localize emergency response agents in real-time under highly dynamic environments when GPS signals are missing or just partially available and also to provide a detailed map of the environment afterwards.…”

Section: Introductionmentioning

confidence: 99%

“…Cooperative SLAM strategies [9], [10], [22]- [25] benefit from the complementarity of visual, LiDAR and/or IMU sensors. Indeed, a stand-alone SLAM approach might not be robust enough to maintain an accurate pose under arbitrary conditions.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Indoor Localization and Mapping: Towards Tracking Resilience Through a Multi-SLAM Approach

Cited by 6 publications

References 18 publications

SLAM Overview: From Single Sensor to Heterogeneous Fusion

SLAM Overview: From Single Sensor to Heterogeneous Fusion

Living with Rules: An AR Approach

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

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