An Adaptive Augmented Vision-Based Ellipsoidal SLAM for Indoor Environments

Lahemer, Elfituri S.; Rad, A.B.

doi:10.3390/s19122795

Cited by 6 publications

(4 citation statements)

References 43 publications

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“…where d is the distance between the two microphones and c is the speed of sound. By substitute τ max from Equation (11) into Equation (10), the angle based on the signal propagation time difference can be calculated as:…”

Section: Active Speaker Localization Using Microphone Arraymentioning

confidence: 99%

“…In this project, the Microsoft HoloLens-Mixed Reality landmark-based SLAM (HoloSLAM) is utilized along with the ellipsoidal set-membership filter method [11] to address the challenges associated with landmarks in landmark-based acoustic-based SLAM. This approach allows for accurate robot localization and mapping even without multiple sound sources are required.…”

Section: Audio-based Ellipsoidal Virtual Holoslam Algorithm Implement...mentioning

confidence: 99%

“…Within indoor environments, the majority of SLAM and navigation systems depend on visual data. Vision imparts a wide range of capabilities to robots, rendering cameras a universally integrative component [11]. However, vision-based SLAM faces limitations like a restricted field of view and occlusion, hampering target exploration.…”

Section: Introductionmentioning

confidence: 99%

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An Audio-Based SLAM for Indoor Environments: A Robotic Mixed Reality Presentation

Lahemer,

Rad

2024

Sensors

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In this paper, we present a novel approach referred to as the audio-based virtual landmark-based HoloSLAM. This innovative method leverages a single sound source and microphone arrays to estimate the voice-printed speaker’s direction. The system allows an autonomous robot equipped with a single microphone array to navigate within indoor environments, interact with specific sound sources, and simultaneously determine its own location while mapping the environment. The proposed method does not require multiple audio sources in the environment nor sensor fusion to extract pertinent information and make accurate sound source estimations. Furthermore, the approach incorporates Robotic Mixed Reality using Microsoft HoloLens to superimpose landmarks, effectively mitigating the audio landmark-related issues of conventional audio-based landmark SLAM, particularly in situations where audio landmarks cannot be discerned, are limited in number, or are completely missing. The paper also evaluates an active speaker detection method, demonstrating its ability to achieve high accuracy in scenarios where audio data are the sole input. Real-time experiments validate the effectiveness of this method, emphasizing its precision and comprehensive mapping capabilities. The results of these experiments showcase the accuracy and efficiency of the proposed system, surpassing the constraints associated with traditional audio-based SLAM techniques, ultimately leading to a more detailed and precise mapping of the robot’s surroundings.

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Section: Active Speaker Localization Using Microphone Arraymentioning

confidence: 99%

Section: Audio-based Ellipsoidal Virtual Holoslam Algorithm Implement...mentioning

confidence: 99%

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An Audio-Based SLAM for Indoor Environments: A Robotic Mixed Reality Presentation

Lahemer,

Rad

2024

Sensors

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“…Therefore, researchers have adopted the idea of loosely coupled fusion and used filters to fuse vision and IMU information to construct visual inertial SLAM algorithms. For example, the visual inertial SLAM algorithm in the literature [ 4 , 5 , 6 , 7 ] regards the IMU-based inertial navigation algorithm as the state prediction equation of the extended Kalman filter (EKF), and it uses the result of pure visual pose estimation as the measurement update equation of the EKF to realize the loosely coupled fusion of vision and IMU. Loosely coupled fusion means that the IMU and the camera estimate motion separately and then fuse their pose estimation results.…”

Section: Introductionmentioning

confidence: 99%

VILO SLAM: Tightly Coupled Binocular Vision–Inertia SLAM Combined with LiDAR

Peng

Zhou

et al. 2023

Sensors

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For the existing visual–inertial SLAM algorithm, when the robot is moving at a constant speed or purely rotating and encounters scenes with insufficient visual features, problems of low accuracy and poor robustness arise. Aiming to solve the problems of low accuracy and robustness of the visual inertial SLAM algorithm, a tightly coupled vision-IMU-2D lidar odometry (VILO) algorithm is proposed. Firstly, low-cost 2D lidar observations and visual–inertial observations are fused in a tightly coupled manner. Secondly, the low-cost 2D lidar odometry model is used to derive the Jacobian matrix of the lidar residual with respect to the state variable to be estimated, and the residual constraint equation of the vision-IMU-2D lidar is constructed. Thirdly, the nonlinear solution method is used to obtain the optimal robot pose, which solves the problem of how to fuse 2D lidar observations with visual–inertial information in a tightly coupled manner. The results show that the algorithm still has reliable pose-estimation accuracy and robustness in many special environments, and the position error and yaw angle error are greatly reduced. Our research improves the accuracy and robustness of the multi-sensor fusion SLAM algorithm.

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Designing a Shared Workspace for Learning Using Augmented Reality and Social Robots

Pasalidou

Fachantidis

2022

Communications in Computer and Information Science

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An Adaptive Augmented Vision-Based Ellipsoidal SLAM for Indoor Environments

Cited by 6 publications

References 43 publications

An Audio-Based SLAM for Indoor Environments: A Robotic Mixed Reality Presentation

An Audio-Based SLAM for Indoor Environments: A Robotic Mixed Reality Presentation

VILO SLAM: Tightly Coupled Binocular Vision–Inertia SLAM Combined with LiDAR

Designing a Shared Workspace for Learning Using Augmented Reality and Social Robots

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