Adaptive Multimodal Localisation Techniques for Mobile Robots in Unstructured Environments : A Review

Mahony, Niall O’; Campbell, Sean; Carvalho, Anderson; Harapanahalli, Suman; Velasco-Hernandez, Gustavo; Riordan, Daniel; Walsh, Joseph T.

doi:10.1109/wf-iot.2019.8767330

Cited by 13 publications

(5 citation statements)

References 28 publications

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“…A diverse range of different methods have been proposed for 3D object recognition. Upon review, we note five types of architecture being developed: they are being view-based, voxel-based, convolution-based, point-based and graph-based [25]. One of the main difficulties in applying CNNs to 3D data is that the spatial relationship between features is lost due to the max-pooling layers.…”

Section: D Deep Learningmentioning

confidence: 99%

Two- and Three-Dimensional Computer Vision Techniques for More Reliable Body Condition Scoring

Mahony

Krpálková

Sayers

et al. 2022

Dairy

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This article identifies the essential technologies and considerations for the development of an Automated Cow Monitoring System (ACMS) which uses 3D camera technology for the assessment of Body Condition Score (BCS). We present a comparison of a range of common techniques at the different developmental stages of Computer Vision including data pre-processing and the implementation of Deep Learning for both 2D and 3D data formats commonly captured by 3D cameras. This research focuses on attaining better reliability from one deployment of an ACMS to the next and proposes a Geometric Deep Learning (GDL) approach and evaluating model performance for robustness from one farm to another in the presence of background, farm, herd, camera pose and cow pose variabilities.

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Section: D Deep Learningmentioning

confidence: 99%

Two- and Three-Dimensional Computer Vision Techniques for More Reliable Body Condition Scoring

Mahony

Krpálková

Sayers

et al. 2022

Dairy

Self Cite

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“…Surveys dedicated to positioning solutions for autonomous robots [4,5] present in-depth information on this topic.…”

Section: Autonomous Robotsmentioning

confidence: 99%

“…Computing Time and Platform Accuracy [92] ICL-NUIMdataset [138] and TUM dataset [139] 296 ms to find the most similar frame and 277 ms to estimate the final pose on Intel Xeon E5-1650 v3 CPU 3.5 GHz, NVidia TITAN GPU more than 80% of the images are localized within 2. 5 If WiFi signals, inertial sensors, beacons, or other sensors can increase the accuracy of marker based localization solutions or can help reduce the number of synthesized images that should be placed on the ceiling/floor/walls of the building (as discussed in Section 3.2.6), a hybrid approach can be even more useful when dealing with natural features from the environment. Acquiring additional information from various sensors can help reduce the search space in the image matching stages.…”

Section: Characteristicsmentioning

confidence: 99%

“…In recent years, the field of indoor localization has increased in popularity due to both the increasing number of applications [1] in domains such as surveillance [2], navigation (both assistive and general purpose) [3], robotics [4][5][6], and Augmented Reality (AR) [7] and the many proposed solutions that differ in terms of the devices used for tracking, the type of sensor data, and the localization algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Even though a considerable number of surveys on indoor localization have been published [1,[4][5][6][9][10][11][12][13][14][15], as this research space continuously developed and the types of localization solutions diversified, we find that for the area of vision based localization, the majority of the previous surveys could have better focus as they are too general (encompassing all kinds of sensing devices) or too specific (addressing only a segment of the vision based localization problem, such as Simultaneous Localization and Mapping (SLAM) [16][17][18][19][20][21], Structure from Motion (SfM) [22], or image matching [23]). Other surveys discuss indoor positioning solutions particular to certain application domains.…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision

Morar

Moldoveanu

Mocanu

et al. 2020

Sensors

112

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Computer vision based indoor localization methods use either an infrastructure of static cameras to track mobile entities (e.g., people, robots) or cameras attached to the mobile entities. Methods in the first category employ object tracking, while the others map images from mobile cameras with images acquired during a configuration stage or extracted from 3D reconstructed models of the space. This paper offers an overview of the computer vision based indoor localization domain, presenting application areas, commercial tools, existing benchmarks, and other reviews. It provides a survey of indoor localization research solutions, proposing a new classification based on the configuration stage (use of known environment data), sensing devices, type of detected elements, and localization method. It groups 70 of the most recent and relevant image based indoor localization methods according to the proposed classification and discusses their advantages and drawbacks. It highlights localization methods that also offer orientation information, as this is required by an increasing number of applications of indoor localization (e.g., augmented reality).

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Hybrid Localization Solution for Autonomous Mobile Robots in Complex Environments

Rebelo,

Valente,

Oliveira

et al. 2024

Lecture Notes in Networks and Systems

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Adaptive Multimodal Localisation Techniques for Mobile Robots in Unstructured Environments : A Review

Cited by 13 publications

References 28 publications

Two- and Three-Dimensional Computer Vision Techniques for More Reliable Body Condition Scoring

Two- and Three-Dimensional Computer Vision Techniques for More Reliable Body Condition Scoring

A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision

Hybrid Localization Solution for Autonomous Mobile Robots in Complex Environments

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