scholarly journals Condition-Invariant Robot Localization Using Global Sequence Alignment of Deep Features

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4103
Author(s):  
Junghyun Oh ◽  
Changwan Han ◽  
Seunghwan Lee
Keyword(s):  
Sequence Alignment ◽  
Autonomous Navigation ◽  
Large Scale ◽  
Visual Localization ◽  
Robot Localization ◽  
Long Term Conditions ◽  
Feature Extraction Method ◽  
Localization And Mapping ◽  
Robust Feature Extraction

Localization is one of the essential process in robotics, as it plays an important role in autonomous navigation, simultaneous localization, and mapping for mobile robots. As robots perform large-scale and long-term operations, identifying the same locations in a changing environment has become an important problem. In this paper, we describe a robust visual localization system under severe appearance changes. First, a robust feature extraction method based on a deep variational autoencoder is described to calculate the similarity between images. Then, a global sequence alignment is proposed to find the actual trajectory of the robot. To align sequences, local fragments are detected from the similarity matrix and connected using a rectangle chaining algorithm considering the robot’s motion constraint. Since the chained fragments provide reliable clues to find the global path, false matches on featureless structures or partial failures during the alignment could be recovered and perform accurate robot localization in changing environments. The presented experimental results demonstrated the benefits of the proposed method, which outperformed existing algorithms in long-term conditions.

Teaching-Playback Navigation Without a Consistent Map

2018 ◽  
Vol 30 (4) ◽  
pp. 591-597 ◽  
Author(s):  
Naoki Akai ◽  
Luis Yoichi Morales ◽  
Hiroshi Murase ◽  
◽  
Keyword(s):  
Autonomous Navigation ◽  
Large Scale ◽  
Mobile Robot Navigation ◽  
Autonomous Mobile Robot ◽  
Environment Map ◽  
Localization And Mapping ◽  
Mapping Process ◽  
Line Processes ◽  
Target Path ◽  
Navigation Method

This paper presents a teaching-playback navigation method that does not require a consistent map built using simultaneous localization and mapping (SLAM). Many open source projects related to autonomous navigation including SLAM have been made available recently; however, autonomous mobile robot navigation in large-scale environments is still difficult because it is difficult to build a consistent map. The navigation method presented in this paper uses several partial maps to represent an environment map. In other words, the complex mapping process is not necessary to begin autonomous navigation. In addition, the trajectory that the robot travels in the mapping phase can be directly used as a target path. As a result, teaching-playback autonomous navigation can be achieved without any off-line processes. We tested the navigation method using log data taken in the environment of the Tsukuba Challenge and the testing results show its performance. We provide source code for the navigation method, which includes modules required for autonomous navigation (https://github.com/NaokiAkai/AutoNavi).

scholarly journals LONG TERM CONDITIONS AND MENTAL HEALTH: AN AUDIT OF LOCAL DATA

2021 ◽  
pp. 32-38
Author(s):  
HANA MORRISSEY ◽  
OLUTAYO ARIKAWE ◽  
PAMELA PAUL ◽  
MANJINDER SANDHU ◽  
ZAIN SADIQUE ◽  
...  
Keyword(s):  
Mental Health ◽  
Large Scale ◽  
Local Population ◽  
Pharmacological Therapy ◽  
Health Care Facilities ◽  
Community Pharmacies ◽  
Local Data ◽  
Long Term Conditions ◽  
Screening Programs

Objective: Studies have shown that mental health is affected by poor physical health, with people living in the deprived area are the most affected. Community Pharmacists potentially have a new role in supporting people with mental illness and dementia to manage their medications. The aim of this local audit was to compare the local population to the national and global population, to inform the development and provision of local pharmacy mental health screening services, to support patients diagnosed with long-term conditions. Methods: This project was designed as an audit of anonymised local data, to inform the development of services offered by community pharmacies to improve adherence to therapy amongst patients diagnosed with long-term conditions in the Black Country, UK. It forms part of a larger study granted ethical approval by the Health Research Authority in 2018. It was carried out against the background of the Covid-19 epidemic. A total of 652 patients pharmacy records were reviewed between March and April 2020. No patient identifiers were included in the reviewed data. Results: This means that the results of this analysis might not be applicable to the entire local population outside the 31-90 y of age range. Conclusion: It is was demonstrated during COVID-19 that pharmacists are well-positioned as easily accessible health care facilities to support patients, especially when the other NHS facilities are stretched or closed. Community pharmacies are in a position to offer large-scale screening programs such as self-completed anxiety, depression and cognitive function screening surveys and refer to general practitioners for further investigations. It is also recommended that the New Medicines Service include mental health disorder patients prescribed pharmacological therapy and to allow the pharmacists appropriate access to medical records to facilitate safe, integrated and effective patient care.

scholarly journals Visual Place Recognition via Robust ℓ2-Norm Distance Based Holism and Landmark Integration

2019 ◽  
Vol 33 ◽  
pp. 8034-8041 ◽  
Author(s):  
Kai Liu ◽  
Hua Wang ◽  
Fei Han ◽  
Hao Zhang
Keyword(s):  
Large Scale ◽  
Optimization Problem ◽  
Data Sets ◽  
Place Recognition ◽  
Benchmark Data ◽  
Localization And Mapping ◽  
Novel Method ◽  
Visual Place Recognition ◽  
Holistic Representation

Visual place recognition is essential for large-scale simultaneous localization and mapping (SLAM). Long-term robot operations across different time of the days, months, and seasons introduce new challenges from significant environment appearance variations. In this paper, we propose a novel method to learn a location representation that can integrate the semantic landmarks of a place with its holistic representation. To promote the robustness of our new model against the drastic appearance variations due to long-term visual changes, we formulate our objective to use non-squared ℓ2-norm distances, which leads to a difficult optimization problem that minimizes the ratio of the ℓ2,1-norms of matrices. To solve our objective, we derive a new efficient iterative algorithm, whose convergence is rigorously guaranteed by theory. In addition, because our solution is strictly orthogonal, the learned location representations can have better place recognition capabilities. We evaluate the proposed method using two large-scale benchmark data sets, the CMU-VL and Nordland data sets. Experimental results have validated the effectiveness of our new method in long-term visual place recognition applications.

scholarly journals LONG-TERM VISUAL LOCALIZATION IN LARGE SCALE URBAN ENVIRONMENTS EXPLOITING STREET LEVEL IMAGERY

2020 ◽  
Vol V-2-2020 ◽  
pp. 57-63
Author(s):  
J. Meyer ◽  
D. Rettenmund ◽  
S. Nebiker
Keyword(s):  
Large Scale ◽  
3D Structure ◽  
Urban Environments ◽  
Visual Localization ◽  
Street Level ◽  
Reference Images ◽  
And Performance ◽  
Localization Approach ◽  
Better Than

Abstract. In this paper, we present our approach for robust long-term visual localization in large scale urban environments exploiting street level imagery. Our approach consists of a 2D-image based localization using image retrieval (NetVLAD) to select reference images. This is followed by a 3D-structure based localization with a robust image matcher (DenseSfM) for accurate pose estimation. This visual localization approach is evaluated by means of the ‘Sun’ subset of the RobotCar seasons dataset, which is part of the Visual Localization benchmark. As the results on the RobotCar benchmark dataset are nearly on par with the top ranked approaches, we focused our investigations on reproducibility and performance with own data. For this purpose, we created a dataset with street-level imagery. In order to have independent reference and query images, we used a road-based and a tram-based mapping campaign with a time difference of four years. The approximately 90% successfully oriented images of both datasets are a good indicator for the robustness of our approach. With about 50% success rate, every second image could be localized with a position accuracy better than 0.25 m and a rotation accuracy better than 2°.

scholarly journals Old-Fashioned Technology in the Era of “Bling”: Is There a Future for Text Messaging in Health Care?

10.2196/16630 ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. e16630 ◽  
Author(s):  
Jane C Willcox ◽  
Rosie Dobson ◽  
Robyn Whittaker
Keyword(s):  
Behavior Change ◽  
Text Messaging ◽  
Large Scale ◽  
Text Message ◽  
Health Behavior Change ◽  
Text Messages ◽  
Long Term Conditions ◽  
Healthy Behavior ◽  
Positive Effects

In the quest to discover the next high-technology solution to solve many health problems, proven established technologies are often overlooked in favor of more “technologically advanced” systems that have not been fully explored for their applicability to support behavior change theory, or used by consumers. Text messages or SMS is one example of an established technology still used by consumers, but often overlooked as part of the mobile health (mHealth) toolbox. The purpose of this paper is to describe the benefits of text messages as a health promotion modality and to advocate for broader scale implementation of efficacious text message programs. Text messaging reaches consumers in a ubiquitous real-time exchange, contrasting the multistep active engagement required for apps and wearables. It continues to be the most widely adopted and least expensive mobile phone function. As an intervention modality, text messaging has taught researchers substantial lessons about tailored interactive health communication; reach and engagement, particularly in low-resource settings; and embedding of behavior change models into digital health. It supports behavior change techniques such as reinforcement, prompts and cues, goal setting, feedback on performance, support, and progress review. Consumers have provided feedback to indicate that text messages can provide them with useful information, increase perceived support, enhance motivation for healthy behavior change, and provide prompts to engage in health behaviors. Significant evidence supports the effectiveness of text messages alone as part of an mHealth toolbox or in combination with health services, to support healthy behavior change. Systematic reviews have consistently reported positive effects of text message interventions for health behavior change and disease management including smoking cessation, medication adherence, and self-management of long-term conditions and health, including diabetes and weight loss. However, few text message interventions are implemented on a large scale. There is still much to be learned from investing in text messaging delivered research. When a modality is known to be effective, we should be learning from large-scale implementation. Many other technologies currently suffer from poor long-term engagement, the digital divide within society, and low health and technology literacy of users. Investing in and incorporating the learnings and lessons from large-scale text message interventions will strengthen our way forward in the quest for the ultimate digitally delivered behavior change model.

scholarly journals Efficient Stereo Visual Simultaneous Localization and Mapping for an Autonomous Unmanned Forklift in an Unstructured Warehouse

2020 ◽  
Vol 10 (2) ◽  
pp. 698 ◽  
Author(s):  
Feiren Wang ◽  
Enli Lü ◽  
Yu Wang ◽  
Guangjun Qiu ◽  
Huazhong Lu
Keyword(s):  
Real Time ◽  
Autonomous Navigation ◽  
Large Scale ◽  
Indirect Method ◽  
Direct Method ◽  
Operating Time ◽  
Simultaneous Localization And Mapping ◽  
High Accuracy ◽  
Image Alignment ◽  
Localization And Mapping

The autonomous navigation of unmanned vehicles in GPS denied environments is an incredibly challenging task. Because cameras are low in price, obtain rich information, and passively sense the environment, vision based simultaneous localization and mapping (VSLAM) has great potential to solve this problem. In this paper, we propose a novel VSLAM framework based on a stereo camera. The proposed approach combines the direct and indirect method for the real-time localization of an autonomous forklift in a non-structured warehouse. Our proposed hybrid method uses photometric errors to perform image alignment for data association and pose estimation, extracts features from keyframes, and matches them to acquire the updated pose. By combining the efficiency of the direct method and the high accuracy of the indirect method, the approach achieves higher speed with comparable accuracy to a state-of-the-art method. Furthermore, the two step dynamic threshold feature extraction method significantly reduces the operating time. In addition, a motion model of the forklift is proposed to provide a more reasonable initial pose for direct image alignment based on photometric errors. The proposed algorithm is experimentally tested on a dataset constructed from a large scale warehouse with dynamic lighting and long corridors, and the results show that it can still successfully perform with high accuracy. Additionally, our method can operate in real time using limited computing resources.

scholarly journals Simulation and Experimental Evaluation of the EKF Simultaneous Localization and Mapping Algorithm on the Wifibot Mobile Robot

2018 ◽  
Vol 8 (2) ◽  
pp. 91-101 ◽  
Author(s):  
Noura Ayadi ◽  
Nabil Derbel ◽  
Nicolas Morette ◽  
Cyril Novales ◽  
Gérard Poisson
Keyword(s):  
Mobile Robot ◽  
Autonomous Navigation ◽  
Large Scale ◽  
Simultaneous Localization And Mapping ◽  
Research Field ◽  
Small Scale ◽  
Mapping Algorithm ◽  
Highly Active ◽  
Localization And Mapping ◽  
Active Research

Abstract In recent years, autonomous navigation for mobile robots has been considered a highly active research field. Within this context, we are interested to apply the Simultaneous Localization And Mapping (SLAM) approach for a wheeled mobile robot. The Extended Kalman Filter has been chosen to perform the SLAM algorithm. In this work, we explicit all steps of the approach. Performances of the developed algorithm have been assessed through simulation in the case of a small scale map. Then, we present several experiments on a real robot that are proceeded in order to exploit a programmed SLAM unit and to generate the navigation map. Based on experimental results, simulation of the SLAM method in the case of a large scale map is then realized. Obtained results are exploited in order to evaluate and compare the algorithm’s consistency and robustness for both cases.

scholarly journals Old-Fashioned Technology in the Era of “Bling”: Is There a Future for Text Messaging in Health Care? (Preprint)

2019 ◽  
Author(s):  
Jane C Willcox ◽  
Rosie Dobson ◽  
Robyn Whittaker
Keyword(s):  
Behavior Change ◽  
Text Messaging ◽  
Large Scale ◽  
Text Message ◽  
Health Behavior Change ◽  
Text Messages ◽  
Long Term Conditions ◽  
Healthy Behavior ◽  
Positive Effects

UNSTRUCTURED In the quest to discover the next high-technology solution to solve many health problems, proven established technologies are often overlooked in favor of more “technologically advanced” systems that have not been fully explored for their applicability to support behavior change theory, or used by consumers. Text messages or SMS is one example of an established technology still used by consumers, but often overlooked as part of the mobile health (mHealth) toolbox. The purpose of this paper is to describe the benefits of text messages as a health promotion modality and to advocate for broader scale implementation of efficacious text message programs. Text messaging reaches consumers in a ubiquitous real-time exchange, contrasting the multistep active engagement required for apps and wearables. It continues to be the most widely adopted and least expensive mobile phone function. As an intervention modality, text messaging has taught researchers substantial lessons about tailored interactive health communication; reach and engagement, particularly in low-resource settings; and embedding of behavior change models into digital health. It supports behavior change techniques such as reinforcement, prompts and cues, goal setting, feedback on performance, support, and progress review. Consumers have provided feedback to indicate that text messages can provide them with useful information, increase perceived support, enhance motivation for healthy behavior change, and provide prompts to engage in health behaviors. Significant evidence supports the effectiveness of text messages alone as part of an mHealth toolbox or in combination with health services, to support healthy behavior change. Systematic reviews have consistently reported positive effects of text message interventions for health behavior change and disease management including smoking cessation, medication adherence, and self-management of long-term conditions and health, including diabetes and weight loss. However, few text message interventions are implemented on a large scale. There is still much to be learned from investing in text messaging delivered research. When a modality is known to be effective, we should be learning from large-scale implementation. Many other technologies currently suffer from poor long-term engagement, the digital divide within society, and low health and technology literacy of users. Investing in and incorporating the learnings and lessons from large-scale text message interventions will strengthen our way forward in the quest for the ultimate digitally delivered behavior change model.

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