Enhancing Body-Mounted LiDAR SLAM using an IMU-based Pedestrian Dead Reckoning (PDR) Model

Sadruddin, Hamza; Mahmoud, Ahmed; Atia, Mohamed

doi:10.1109/mwscas48704.2020.9184561

Cited by 15 publications

(6 citation statements)

References 9 publications

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“…For validation of the system, a wearable sensor assembly is designed [20]. Both sensors are mounted on the backplate of self-contained breathing apparatus.…”

Section: Sensor Hardware Assemblymentioning

confidence: 99%

Enhanced Pedestrian Dead Reckoning Sensor Fusion for Firefighting

Augustin,

Ossmann

2023

Ecsa 2023

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“…For validation of the system, a wearable sensor assembly is designed [20]. Both sensors are mounted on the backplate of self-contained breathing apparatus.…”

Section: Sensor Hardware Assemblymentioning

confidence: 99%

Enhanced Pedestrian Dead Reckoning Sensor Fusion for Firefighting

Augustin,

Ossmann

2023

Ecsa 2023

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“…Solutions of this type are normally used in GNSS-denied environments, but if such limitations are not present, the position indicated by the GNSS receiver can be additionally used to improve the quality of SLAM-based positioning [34,35]. The pose of the observation unit and its movement can be additionally determined from the input data provided by other sources, such as IMU sensors [36,37] or wheel odometry [38]. SLAM algorithms are found in numerous implementations and are frequently based on the Kalman filters [35,39], graphs [40,41], or voxels [42].…”

Section: Handheld Mobile Laser Scanningmentioning

confidence: 99%

Modern Solution for Fast and Accurate Inventorization of Open-Pit Mines by the Active Remote Sensing Technique—Case Study of Mikoszów Granite Mine (Lower Silesia, SW Poland)

et al. 2021

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Mining industry faces new technological and economic challenges which need to be overcome in order to raise it to a new technological level in accordance with the ideas of Industry 4.0. Mining companies are searching for new possibilities of optimizing and automating processes, as well as for using digital technology and modern computer software to aid technological processes. Every stage of deposit management requires mining engineers, geologists, surveyors, and environment protection specialists who are involved in acquiring, storing, processing, and sharing data related to the parameters describing the deposit, its exploitation and the environment. These data include inter alia: geometries of the deposit, of the excavations, of the overburden and of the mined mineral, borders of the support pillars and of the buffer zones, mining advancements with respect to the set borders, effects of mining activities on the ground surface, documentation of landslide hazards and of the impact of mining operations on the selected elements of the environment. Therefore, over the life cycle of a deposit, modern digital technological solutions should be implemented in order to automate the processes of acquiring, sharing, processing and analyzing data related to deposit management. In accordance with this idea, the article describes the results of a measurement experiment performed in the Mikoszów open-pit granite mine (Lower Silesia, SW Poland) with the use of mobile LiDAR systems. The technology combines active sensors with automatic and global navigation system synchronized on a mobile platform in order to generate an accurate and precise geospatial 3D cloud of points.

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“…Research has been conducted for personal navigation with IMUs mounted in various locations [ 11 ]. There have also been efforts to incorporate LiDAR into a wearable sensor for localization [ 12 ]. This LiDAR technique differs from the previously mentioned LiDAR in that it involves LiDAR mounted to an individual rather than LiDAR in a fixed position observing the individual.…”

Section: Introductionmentioning

confidence: 99%

Walking Trajectory Estimation Using Multi-Sensor Fusion and a Probabilistic Step Model

Rabb

Steckenrider

2023

Sensors

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This paper presents a framework for accurately and efficiently estimating a walking human’s trajectory using a computationally inexpensive non-Gaussian recursive Bayesian estimator. The proposed framework fuses global and inertial measurements with predictions from a kinematically driven step model to provide robustness in localization. A maximum a posteriori-type filter is trained on typical human kinematic parameters and updated based on live measurements. Local step size estimates are generated from inertial measurement units using the zero-velocity update (ZUPT) algorithm, while global measurements come from a wearable GPS. After each fusion event, a gradient ascent optimizer efficiently locates the highest likelihood of the individual’s location which then triggers the next estimator iteration.The proposed estimator was compared to a state-of-the-art particle filter in several Monte Carlo simulation scenarios, and the original framework was found to be comparable in accuracy and more efficient at higher resolutions. It is anticipated that the methods proposed in this work could be more useful in general real-time estimation (beyond just personal navigation) than the traditional particle filter, especially if the state is many-dimensional. Applications of this research include but are not limited to: in natura biomechanics measurement, human safety in manual fieldwork environments, and human/robot teaming.

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Enhancing Body-Mounted LiDAR SLAM using an IMU-based Pedestrian Dead Reckoning (PDR) Model

Cited by 15 publications

References 9 publications

Enhanced Pedestrian Dead Reckoning Sensor Fusion for Firefighting

Enhanced Pedestrian Dead Reckoning Sensor Fusion for Firefighting

Modern Solution for Fast and Accurate Inventorization of Open-Pit Mines by the Active Remote Sensing Technique—Case Study of Mikoszów Granite Mine (Lower Silesia, SW Poland)

Walking Trajectory Estimation Using Multi-Sensor Fusion and a Probabilistic Step Model

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