UWB/LiDAR Fusion For Cooperative Range-Only SLAM

Song, Yang; Guan, Mingyang; Tay, Wee Peng; Law, Choi Look; Chen, Wen

doi:10.1109/icra.2019.8794222

Cited by 82 publications

(45 citation statements)

References 24 publications

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“…This algorithm falsely estimated that the robot had arrived at entrance of the hall (indicated by the green arrow in Figure 6B); however, the ground truth position of the hall's entrance was at the intersections between the paths and the white vertical line in Figure 6B. Similar cases of poor estimation in long corridors have been discussed in the study of Song et al (2019).…”

Section: Resultsmentioning

confidence: 54%

Assistive Navigation Using Deep Reinforcement Learning Guiding Robot With UWB/Voice Beacons and Semantic Feedbacks for Blind and Visually Impaired People

Liu

Huang

et al. 2021

Front. Robot. AI

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Facilitating navigation in pedestrian environments is critical for enabling people who are blind and visually impaired (BVI) to achieve independent mobility. A deep reinforcement learning (DRL)–based assistive guiding robot with ultrawide-bandwidth (UWB) beacons that can navigate through routes with designated waypoints was designed in this study. Typically, a simultaneous localization and mapping (SLAM) framework is used to estimate the robot pose and navigational goal; however, SLAM frameworks are vulnerable in certain dynamic environments. The proposed navigation method is a learning approach based on state-of-the-art DRL and can effectively avoid obstacles. When used with UWB beacons, the proposed strategy is suitable for environments with dynamic pedestrians. We also designed a handle device with an audio interface that enables BVI users to interact with the guiding robot through intuitive feedback. The UWB beacons were installed with an audio interface to obtain environmental information. The on-handle and on-beacon verbal feedback provides points of interests and turn-by-turn information to BVI users. BVI users were recruited in this study to conduct navigation tasks in different scenarios. A route was designed in a simulated ward to represent daily activities. In real-world situations, SLAM-based state estimation might be affected by dynamic obstacles, and the visual-based trail may suffer from occlusions from pedestrians or other obstacles. The proposed system successfully navigated through environments with dynamic pedestrians, in which systems based on existing SLAM algorithms have failed.

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Section: Resultsmentioning

confidence: 54%

Assistive Navigation Using Deep Reinforcement Learning Guiding Robot With UWB/Voice Beacons and Semantic Feedbacks for Blind and Visually Impaired People

Liu

Huang

et al. 2021

Front. Robot. AI

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“…Note that the power consumption presented is for the overall system including the sensors. As can be seen from Table II, only UWB-LiDAR [17] matches the accuracy and latency provided by FEEL, however this comes at the expense of high power consumption and overall system cost since it employs LiDAR. The missing details of [9] is due to the insufficient details provided in the article.…”

Section: B Experimental Resultsmentioning

confidence: 93%

FEEL: Fast, Energy-Efficient Localization for Autonomous Indoor Vehicles

Gokhale

Barrera

Prasad

2021

ICC 2021 - IEEE International Conference on Communications

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Autonomous vehicles have created a sensation in both indoor and outdoor applications. The famous indoor usecase is process automation inside a warehouse using Autonomous Indoor Vehicles (AIV). These vehicles need to locate themselves not only with an accuracy of a few centimeters but also within a few milliseconds in an energy-efficient manner. Due to these challenges, localization is a holy grail. In this paper, we propose FEEL -an indoor localization system that uses a fusion of three low-energy sensors: IMU, UWB, and radar. We provide detailed software and hardware architecture of FEEL. Further, we propose Adaptive Sensing Algorithm (ASA) for optimizing for localization accuracy and energy consumption of FEEL by adjusting the sensing rate to the dynamics of the physical environment in real-time. Our extensive performance evaluation over diverse test settings reveals that FEEL provides a localization accuracy of sub-7 cm with an ultra-low latency of around 3 ms. Additionally, ASA yields up to 20% energy savings with only a marginal trade off in accuracy. Furthermore, we show that FEEL outperforms state of the art in indoor localization.

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“…For instance, the integration of the radio frequency identification (RFID) technology with GPS-based technologies are reviewed to enhance outdoor tracking of resources [12,13]. Data fusion of imaging technologies (e.g., laser scanning and photogrammetry) with technologies such as RFID, UWB, and GPS have also been investigated recently [14,15].…”

Section: Overview Of Research On Automated Site Data Acquisitionmentioning

confidence: 99%

“…In fact, by having a better visualization model of a site and real-time information for progress reporting and schedule updating, it is possible to provide more robust and timely information for an earned value analysis (EVM) [6,15,28]. For instance, researchers in [15] integrated a 2D laser scanner with a UWB system in order to improve their simultaneous localization and mapping (SLAM) algorithm for localization of a moving robot. They demonstrated that this integration provides better visualization of the site due to its ability to accurately measure the range to nearby objects.…”

Section: Point Cloud Data Fusion With Uwb Datamentioning

confidence: 99%

Automated Data Acquisition in Construction with Remote Sensing Technologies

2020

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Near real-time tracking of construction operations and timely progress reporting are essential for effective management of construction projects. This does not only mitigate potential negative impact of schedule delays and cost overruns but also helps to improve safety on site. Such timely tracking circumvents the drawbacks of conventional methods for data acquisition, which are manual, labor-intensive, and not reliable enough for various construction purposes. To address these issues, a wide range of automated site data acquisition, including remote sensing (RS) technologies, has been introduced. This review article describes the capabilities and limitations of various scenarios employing RS enabling technologies for localization, with a focus on multi-sensor data fusion models. In particular, we have considered integration of real-time location systems (RTLSs) including GPS and UWB with other sensing technologies such as RFID, WSN, and digital imaging for their use in construction. This integrated use of technologies, along with information models (e.g., BIM models) is expected to enhance the efficiency of automated site data acquisition. It is also hoped that this review will prompt researchers to investigate fusion-based data capturing and processing.

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UWB/LiDAR Fusion For Cooperative Range-Only SLAM

Cited by 82 publications

References 24 publications

Assistive Navigation Using Deep Reinforcement Learning Guiding Robot With UWB/Voice Beacons and Semantic Feedbacks for Blind and Visually Impaired People

Assistive Navigation Using Deep Reinforcement Learning Guiding Robot With UWB/Voice Beacons and Semantic Feedbacks for Blind and Visually Impaired People

FEEL: Fast, Energy-Efficient Localization for Autonomous Indoor Vehicles

Automated Data Acquisition in Construction with Remote Sensing Technologies

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