Active Perception for Outdoor Localisation with an Omnidirectional Camera

Jayasuriya, Maleen; Ranasinghe, Ravindra; Dissanayake, Gamini

doi:10.1109/iros45743.2020.9340974

Cited by 23 publications

(22 citation statements)

References 42 publications

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“…F I G U R E 7 Position measurement drifts of all robots and a representative velocity measurement error. (30), the average of the sum of d t,all during the simulation decreases by 66.5% with modified controllers (30), (33). The average passing-through time of all robots increases by 43%.…”

Section: Comparative Numerical Simulationsmentioning

confidence: 96%

Robust distributed control within a curve virtual tube for a robotic swarm under self‐localization drift and precise relative navigation

Gao

Bai

Quan

2023

Intl J Robust & Nonlinear

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To guide the movement of a robotic swarm in a corridor-like environment, a curve virtual tube with no obstacle inside was designed in our previous work. This article generalizes the controller design to the condition that all robots have self-localization drifts and precise relative navigation, where the flocking algorithm is introduced to reduce the negative impact of the self-localization drift. Similar to the "many wrongs principle," it is shown that the cohesion behavior and the velocity alignment behavior are able to reduce the influence of the position measurement drift and the velocity measurement error, respectively. For convenience in practical use, a modified vector field controller with five control terms is put forward. Finally, the effectiveness of the proposed method is validated by numerical simulations and real experiments.

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Section: Comparative Numerical Simulationsmentioning

confidence: 96%

Robust distributed control within a curve virtual tube for a robotic swarm under self‐localization drift and precise relative navigation

Gao

Bai

Quan

2023

Intl J Robust & Nonlinear

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show abstract

“…In this study, 3D sparse models reconstructed from street view images were used as the reference data source, and a three-step algorithm was designed for geo-localization, which includes image retrievalbased coarse geo-localization, reliable feature matching between the query image and retrieved candidate images, and the PnP (Perspective n Points) based precise geo-localization. Instead of using low-level features, [144] turned to detect highlevel semantic information from spherical images, such as lamp posts and street signs, which are detected based on a retrained YOLO CNN network. To geo-localizing interesting targets within urban scenes to assist vehicle navigation, [6] proposed a line of bearing (LOB) based positioning method for urban street objects, e.g., road lamps, shown in Fig.…”

Section: B Urban Modeling and Navigationmentioning

confidence: 99%

3D reconstruction of spherical images: A review of techniques, applications, and prospects

Li¹,

Weng²,

You³

et al. 2023

Preprint

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3D reconstruction plays an increasingly important role in modern photogrammetric systems. Conventional satellite or aerial-based remote sensing (RS) platforms can provide the necessary data sources for the 3D reconstruction of large-scale landforms and cities. Even with low-altitude UAVs (Unmanned Aerial Vehicles), 3D reconstruction in complicated situations, such as urban canyons and indoor scenes, is challenging due to frequent tracking failures between camera frames and high data collection costs. Recently, spherical images have been extensively used due to the capability of recording surrounding environments from one camera exposure. In contrast to perspective images with limited FOV (Field of View), spherical images can cover the whole scene with full horizontal and vertical FOV and facilitate camera tracking and data acquisition in these complex scenes. With the rapid evolution and extensive use of professional and consumergrade spherical cameras, spherical images show great potential for the 3D modeling of urban and indoor scenes. Classical 3D reconstruction pipelines, however, cannot be directly used for spherical images. Besides, there exist few software packages that are designed for the 3D reconstruction of spherical images. As a result, this research provides a thorough survey of the state-ofthe-art for 3D reconstruction of spherical images in terms of data acquisition, feature detection and matching, image orientation, and dense matching as well as presenting promising applications and discussing potential prospects. We anticipate that this study offers insightful clues to direct future research.

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“…Current research on vibration signal recognition focuses on feature extraction and classification algorithms, such as IMF, wavelet analysis, and convolutional neural networks [3,8,[14][15][16][17][18]; however, the complex processing algorithms place high demands on the processing platform. For the application scenario of Intelligent manhole cover, the recognition method proposed in this paper has very little computation and can achieve digital IC autonomous data fusion, which effectively reduces the calculation of MCU, avoids the contradiction between the need for MCU to permanently fuse motion sensor data and the low-power requirement of the device, and reduces the power consumption of the system based on the recognition of target events.…”

Section: Manhole Coversmentioning

confidence: 99%

Research and Implementation of Low-Power Anomaly Recogni-Tion Method for Intelligent Manhole Covers

Guo¹,

Wang²,

Sun³

et al. 2023

Preprint

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This paper addresses the difficulty of balancing real-time response and low power consumption in intelligent manhole cover application scenarios. It proposes a method to distinguish normal and abnormal events by segmenting the boundary where the acceleration of the intelligent manhole cover deviates from a set threshold and lasts for a certain period, based on the difference in vibration patterns of the intelligent manhole cover when a normal event and an abnormal event occur. This paper uses digital output motion sensor data autonomous data fusion to implement the pattern mentioned above recognition algorithm, which reduces the MCU computing and working time and the overall power consumption of the system while meeting the real-time response requirements. The test results demonstrate that the method has a high rate of anomaly recognition accuracy. The method ensures the system's real-time response capability, and the actual low power consumption test demonstrates that the device can operate continuously for 9.5 years. The low power consumption index exceeds the requirements of the existing national standard, thereby resolving the issue that it is challenging to balance intelligent manhole cover abnormality recognition and low power consumption.

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Active Perception for Outdoor Localisation with an Omnidirectional Camera

Cited by 23 publications

References 42 publications

Robust distributed control within a curve virtual tube for a robotic swarm under self‐localization drift and precise relative navigation

Robust distributed control within a curve virtual tube for a robotic swarm under self‐localization drift and precise relative navigation

3D reconstruction of spherical images: A review of techniques, applications, and prospects

Research and Implementation of Low-Power Anomaly Recogni-Tion Method for Intelligent Manhole Covers

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