Real-Time RGB-D Camera Pose Estimation in Novel Scenes using a Relocalisation Cascade

Camera pose estimation is an important problem in computer vision. Common techniques either match the current image against keyframes with known poses, directly regress the pose, or establish correspondences between keypoints in the image and points in the scene to estimate the pose. In recent years, regression forests have become a popular alternative to establish such correspondences. They achieve accurate results, but have traditionally needed to be trained offline on the target scene, preventing relocalisation in new environments. Recently, we showed how to circumvent this limitation by adapting a pre-trained forest to a new scene on the fly. The adapted forests achieved relocalisation performance that was on par with that of offline forests, and our approach was able to estimate the camera pose in close to real time. In this paper, we present an extension of this work that achieves significantly better relocalisation performance whilst running fully in real time. To achieve this, we make several changes to the original approach: (i) instead of accepting the camera pose hypothesis without question, we make it possible to score the final few hypotheses using a geometric approach and select the most promising; (ii) we chain several instantiations of our relocaliser together in a cascade, allowing us to try faster but less accurate relocalisation first, only falling back to slower, more accurate relocalisation as necessary; and (iii) we tune the parameters of our cascade to achieve effective overall performance. These changes allow us to significantly improve upon the performance our original state-of-the-art method was able to achieve on the well-known 7-Scenes and Stanford 4 Scenes benchmarks. As additional contributions, we present a way of visualising the internal behaviour of our forests and show how to entirely circumvent the need to pre-train a forest on a generic scene.Comment: Tommaso Cavallari, Stuart Golodetz, Nicholas Lord and Julien Valentin assert joint first authorshi

Legitimacy or Legitimation? Intensive Analysis of Informal Decision-Making Processes of Disaster Response after 3.11 Earthquake

In this paper, based on my research about the mutual aids between local govern-ments after the Great East Japan Disaster (3.11), I try to describe the process in which I have collected data based on typical theories and through the interaction with informants belonging to local governments in order to discuss the possible relationship between data and theories. First of all, I evaluate two recent empirical studies both of which reached one similar conclusion on one of the typical theoretical perspective shared by most researchers on Japanese society after 3.11. I name this perspective “divergent theory” because that perspective should generally point out the divergence of two incompatible forms of norm or narratives on political responses to that disaster. Secondly, I describe the data-producing process in which I have collected the data about the decision-making of mutual aid implementation initially through structured questionnaires which were planned based on those static theories and then I modified these data through face-to-face interviews. Consequently, I have come to interpret these political responses to disaster as convergence rather than divergence referring not only to my own process-produced data but to Luhmann’s sociological theory which was produced by comparative observation of interactions within political processes. Finally, I describe reactions of my informants who were introduced to my theoretical interpretation as part of the open-ended process of a reflexive relationship between data and theories in my research, which should be called “action research.

Discovering location based services: A unified approach for heterogeneous indoor localization systems

The technological solutions and communication capabilities offered by the Internet of Things paradigm, in terms of raising availability of wearable devices, the ubiquitous internet connection, and the presence on the market of service-oriented solutions, have allowed a wide proposal of Location Based Services (LBS). In a close future, we foresee that companies and service providers will have developed reliable solutions to address indoor positioning, as basis for useful location based services. These solutions will be different from each other and they will adopt different hardware and processing techniques. This paper describes the proposal of a unified approach for Indoor Localization Systems that enables the cooperation between heterogeneous solutions and their functional modules. To this end, we designed an integrated architecture that, abstracting its main components, allows a seamless interaction among them. Finally, we present a working prototype of such architecture, which is based on the popular Telegram application for Android, as an integration demonstrator. The integration of the three main phases –namely the discovery phase, the User Agent self-configuration, and the indoor map retrieval/rendering– demonstrates the feasibility of the proposed integrated architectur

Towards Improved Proxy-based Deep Metric Learning via Data-Augmented Domain Adaptation

Deep Metric Learning (DML) plays an important role in modern computer vision research, where we learn a distance metric for a set of image representations. Recent DML techniques utilize the proxy to interact with the corresponding image samples in the embedding space. However, existing proxy-based DML methods focus on learning individual proxy-to-sample distance while the overall distribution of samples and proxies lacks attention. In this paper, we present a novel proxy-based DML framework that focuses on aligning the sample and proxy distributions to improve the efficiency of proxy-based DML losses. Specifically, we propose the Data-Augmented Domain Adaptation (DADA) method to adapt the domain gap between the group of samples and proxies. To the best of our knowledge, we are the first to leverage domain adaptation to boost the performance of proxy-based DML. We show that our method can be easily plugged into existing proxy-based DML losses. Our experiments on benchmarks, including the popular CUB-200-2011, CARS196, Stanford Online Products, and In-Shop Clothes Retrieval, show that our learning algorithm significantly improves the existing proxy losses and achieves superior results compared to the existing methods.Comment: Accepted by AAAI 202

AGI for Agriculture

Artificial General Intelligence (AGI) is poised to revolutionize a variety of sectors, including healthcare, finance, transportation, and education. Within healthcare, AGI is being utilized to analyze clinical medical notes, recognize patterns in patient data, and aid in patient management. Agriculture is another critical sector that impacts the lives of individuals worldwide. It serves as a foundation for providing food, fiber, and fuel, yet faces several challenges, such as climate change, soil degradation, water scarcity, and food security. AGI has the potential to tackle these issues by enhancing crop yields, reducing waste, and promoting sustainable farming practices. It can also help farmers make informed decisions by leveraging real-time data, leading to more efficient and effective farm management. This paper delves into the potential future applications of AGI in agriculture, such as agriculture image processing, natural language processing (NLP), robotics, knowledge graphs, and infrastructure, and their impact on precision livestock and precision crops. By leveraging the power of AGI, these emerging technologies can provide farmers with actionable insights, allowing for optimized decision-making and increased productivity. The transformative potential of AGI in agriculture is vast, and this paper aims to highlight its potential to revolutionize the industry

Real-time RGB-D camera pose estimation in novel scenes using a relocalisation cascade

Camera pose estimation is an important problem in computer vision. Common techniques either match the current image against keyframes with known poses, directly regress the pose, or establish correspondences between keypoints in the image and points in the scene to estimate the pose. In recent years, regression forests have become a popular alternative to establish such correspondences. They achieve accurate results, but have traditionally needed to be trained offline on the target scene, preventing relocalisation in new environments. Recently, we showed how to circumvent this limitation by adapting a pre-trained forest to a new scene on the fly. The adapted forests achieved relocalisation performance that was on par with that of offline forests, and our approach was able to estimate the camera pose in close to real time. In this paper, we present an extension of this work that achieves significantly better relocalisation performance whilst running fully in real time. To achieve this, we make several changes to the original approach: (i) instead of accepting the camera pose hypothesis without question, we make it possible to score the final few hypotheses using a geometric approach and select the most promising; (ii) we chain several instantiations of our relocaliser together in a cascade, allowing us to try faster but less accurate relocalisation first, only falling back to slower, more accurate relocalisation as necessary; and (iii) we tune the parameters of our cascade to achieve effective overall performance. These changes allow us to significantly improve upon the performance our original state-of-the-art method was able to achieve on the well-known 7-Scenes and Stanford 4 Scenes benchmarks. As additional contributions, we present a way of visualising the internal behaviour of our forests and show how to entirely circumvent the need to pre-train a forest on a generic scene

Localization and Mapping for Self-Driving Vehicles:A Survey

The upsurge of autonomous vehicles in the automobile industry will lead to better driving experiences while also enabling the users to solve challenging navigation problems. Reaching such capabilities will require significant technological attention and the flawless execution of various complex tasks, one of which is ensuring robust localization and mapping. Recent surveys have not provided a meaningful and comprehensive description of the current approaches in this field. Accordingly, this review is intended to provide adequate coverage of the problems affecting autonomous vehicles in this area, by examining the most recent methods for mapping and localization as well as related feature extraction and data security problems. First, a discussion of the contemporary methods of extracting relevant features from equipped sensors and their categorization as semantic, non-semantic, and deep learning methods is presented. We conclude that representativeness, low cost, and accessibility are crucial constraints in the choice of the methods to be adopted for localization and mapping tasks. Second, the survey focuses on methods to build a vehicle’s environment map, considering both the commercial and the academic solutions available. The analysis proposes a difference between two types of environment, known and unknown, and develops solutions in each case. Third, the survey explores different approaches to vehicles’ localization and also classifies them according to their mathematical characteristics and priorities. Each section concludes by presenting the related challenges and some future directions. The article also highlights the security problems likely to be encountered in self-driving vehicles, with an assessment of possible defense mechanisms that could prevent security attacks in vehicles. Finally, the article ends with a debate on the potential impacts of autonomous driving, spanning energy consumption and emission reduction, sound and light pollution, integration into smart cities, infrastructure optimization, and software refinement. This thorough investigation aims to foster a comprehensive understanding of the diverse implications of autonomous driving across various domains