LiDAR Technology for Human Activity Recognition: Outlooks and Challenges

Rinchi, Omar; Ghazzai, Hakim; Alsharoa, Ahmad; Massoud, Yehia

doi:10.1109/iotm.001.2200199

Cited by 11 publications

(1 citation statement)

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“…New generations of vision sensors, such as LiDARs, are capable of converting the surrounding environment into a three-dimensional (3D) representation of point clouds (Rinchi et al 2023). Unlike traditional red-green-blue (RGB) pixel-based video cameras, LiDARs do not collect personal identifiable information (PII), which protects the privacy of humans in their surroundings.…”

Section: Infrastructure-based Sensorsmentioning

confidence: 99%

Single-Snapshot Localization for Near-Field RIS Model Using Atomic Norm Minimization

Rinchi

Elzanaty

Alsharoa

2022

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

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Reconfigurable intelligent surfaces (RISs) are expected to play a significant role in the next generation of wireless cellular technology. This paper proposes an uplink localization scheme using a single-snapshot solution for user equipment (UE) that is located in the near-field of the RIS. We propose utilizing the atomic norm minimization method to achieve super-resolution localization accuracy. We formulate an optimization problem to estimate the UE location parameters (i.e., angles and distances) by minimizing the atomic norm. Then, we propose to exploit strong duality to solve the atomic norm problem using the dual problem and semidefinite programming (SDP). The RIS is controlled and designed using estimated parameters to enhance the beamforming capabilities. Finally, we compare the localization performance of the proposed atomic norm minimization with compressed sensing (CS) in terms of the localization error. The numerical results show a superior performance of the proposed atomic norm method over the CS where a sub-cm level of accuracy can be achieved under some of the system configuration conditions using the proposed atomic norm method.

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Section: Infrastructure-based Sensorsmentioning

confidence: 99%

Single-Snapshot Localization for Near-Field RIS Model Using Atomic Norm Minimization

Rinchi

Elzanaty

Alsharoa

2022

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

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Enhancing Indoor Robot Pedestrian Detection Using Improved PIXOR Backbone and Gaussian Heatmap Regression in 3D LiDAR Point Clouds

Nguyen,

Hoang,

Nguyen

et al. 2024

IEEE Access

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Accurate and robust pedestrian detection is fundamental for indoor robotic systems to navigate safely and seamlessly alongside humans in spatially constrained, unpredictable indoor environments. This paper presents a novel method, IRBGHR-PIXOR, a detection framework specifically engineered for pedestrian perception in indoor mobile robots. This novel approach employs an enhanced adaptation of the cutting-edge PIXOR model, integrating two pivotal augmentations: a remodeled convolutional backbone leveraging Inverted Residual Blocks (IRB) in unison with Gaussian Heatmap Regression (GHR), as well as a Modified Focal Loss (MFL) function to tackle data imbalance issues. The IRB component notably bolsters the network's aptitude for processing intricate spatial representations generated from sparse 3D LiDAR scans. Meanwhile, integrating GHR further elevates accuracy by enabling precise localization of pedestrian subjects. This is achieved by modeling the probability distribution and predicting the central location of individuals in the point cloud data. Extensively evaluated on the large-scale JRDB dataset comprising intense scans from 16-beam Velodyne LiDAR sensors, IRBGHR-PIXOR accomplishes exceptional results, attaining 97.17% Average Precision (AP) at the 0.5 IOU threshold. Notably, this level of accuracy is achieved without significantly increasing model complexity. By enhancing algorithms to overcome challenges in confined indoor environments, this research paves the way for safe and effective deployment of autonomous technologies once encumbered by perceptual limitations in human-centered spaces. Nonetheless, evaluating performance in diverse edge cases and integration with complementary sensory cues promise continued progress. The developments contribute towards the vital capacity of reliable dynamic perception for next-generation robotic systems coexisting in human-centric environments.

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