Actively Mapping Industrial Structures with Information Gain-Based Planning on a Quadruped Robot

In this paper, we develop an online active mapping system to enable a quadruped robot to autonomously survey large physical structures. We describe the perception, planning and control modules needed to scan and reconstruct an object of interest, without requiring a prior model. The system builds a voxel representation of the object, and iteratively determines the Next-Best-View (NBV) to extend the representation, according to both the reconstruction itself and to avoid collisions with the environment. By computing the expected information gain of a set of candidate scan locations sampled on the as-sensed terrain map, as well as the cost of reaching these candidates, the robot decides the NBV for further exploration. The robot plans an optimal path towards the NBV, avoiding obstacles and un-traversable terrain. Experimental results on both simulated and real-world environments show the capability and efficiency of our system. Finally we present a full system demonstration on the real robot, the ANYbotics ANYmal, autonomously reconstructing a building facade and an industrial structure

InCloud: Incremental Learning for Point Cloud Place Recognition

Place recognition is a fundamental component of robotics, and has seen tremendous improvements through the use of deep learning models in recent years. Networks can experience significant drops in performance when deployed in unseen or highly dynamic environments, and require additional training on the collected data. However naively fine-tuning on new training distributions can cause severe degradation of performance on previously visited domains, a phenomenon known as catastrophic forgetting. In this paper we address the problem of incremental learning for point cloud place recognition and introduce InCloud, a structure-aware distillation-based approach which preserves the higher-order structure of the network's embedding space. We introduce several challenging new benchmarks on four popular and large-scale LiDAR datasets (Oxford, MulRan, In-house and KITTI) showing broad improvements in point cloud place recognition performance over a variety of network architectures. To the best of our knowledge, this work is the first to effectively apply incremental learning for point cloud place recognition.Comment: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022

Uncertainty-Aware Lidar Place Recognition in Novel Environments

State-of-the-art approaches to lidar place recognition degrade significantly when tested on novel environments that are not present in their training dataset. To improve their reliability, we propose uncertainty-aware lidar place recognition, where each predicted place match must have an associated uncertainty that can be used to identify and reject potentially incorrect matches. We introduce a novel evaluation protocol designed to benchmark uncertainty-aware lidar place recognition, and present Deep Ensembles as the first uncertainty-aware approach for this task. Testing across three large-scale datasets and three state-of-the-art architectures, we show that Deep Ensembles consistently improves the performance of lidar place recognition in novel environments. Compared to a standard network, our results show that Deep Ensembles improves the Recall@1 by more than 5% and AuPR by more than 3% on average when tested on previously unseen environments. Our code repository will be made publicly available upon paper acceptance at https://github.com/csiro-robotics/Uncertainty-LPR.Comment: 7 pages, 4 figures. Under Revie

The Impact of Fintech and Artificial Intelligence on COVID 19 and Sustainable Development Goals

Purpose: The main goal of this article is reviewing the importance of Fintech and AI in achieving sustainable development goals such as education, health, equality and etc. and reducing the negative effects of COVID19. Generally, it is necessary to pay special attention to the environment, poverty, education and welfare, and artificial intelligence and finance can complement each other in this regard. Methodology: In this article, we have addressed issues such as digitalization, green finance, climate change, big data, sustainable development parameters, and issues related to artificial intelligence and its potential impact on sustainable development. Finally, we have studied the effects of COVID-19 on Fintech and AI and vice versa. Findings: The results show that Fintech and AI can be effective in achieving the sustainable development goals and they can play an important role in mitigating the harmful effects of COVID-19 in different dimensions such as economic, social health, environment and etc. Originality/Value: The main contribution of the article is that we have focus on the impact of Fintech and AI on SDGs and COVID19 simultaneously which are often examined separately. On the other hand, the author has tried to look at the issue of sustainable development and its challenges from a financial perspective and have a special and comprehensive look

Wild-Places: A Large-Scale Dataset for Lidar Place Recognition in Unstructured Natural Environments

Many existing datasets for lidar place recognition are solely representative of structured urban environments, and have recently been saturated in performance by deep learning based approaches. Natural and unstructured environments present many additional challenges for the tasks of long-term localisation but these environments are not represented in currently available datasets. To address this we introduce Wild-Places, a challenging large-scale dataset for lidar place recognition in unstructured, natural environments. Wild-Places contains eight lidar sequences collected with a handheld sensor payload over the course of fourteen months, containing a total of 67K undistorted lidar submaps along with accurate 6DoF ground truth. Our dataset contains multiple revisits both within and between sequences, allowing for both intra-sequence (i.e. loop closure detection) and inter-sequence (i.e. re-localisation) place recognition. We also benchmark several state-of-the-art approaches to demonstrate the challenges that this dataset introduces, particularly the case of long-term place recognition due to natural environments changing over time. Our dataset and code will be available at https://csiro-robotics.github.io/Wild-Places.Comment: Equal Contribution from first two authors Under Review Website link: https://csiro-robotics.github.io/Wild-Places

Pose-Graph Attentional Graph Neural Network for Lidar Place Recognition

This paper proposes a lidar place recognition approach, called P-GAT, to increase the receptive field between point clouds captured over time. Instead of comparing pairs of point clouds, we compare the similarity between sets of point clouds to use the maximum spatial and temporal information between neighbour clouds utilising the concept of pose-graph SLAM. Leveraging intra- and inter-attention and graph neural network, P-GAT relates point clouds captured in nearby locations in Euclidean space and their embeddings in feature space. Experimental results on the large-scale publically available datasets demonstrate the effectiveness of our approach in recognising scenes lacking distinct features and when training and testing environments have different distributions (domain adaptation). Further, an exhaustive comparison with the state-of-the-art shows improvements in performance gains. Code will be available upon acceptance.Comment: 8 pages, 3 figures, 5 table

Ecological Footprint and Its Determinants in MENA Countries: A Spatial Econometric Approach

Publication history: Accepted - 15 September 2022; Published online - 18 September 2022Countries in the Middle East and North Africa (MENA) have been facing serious environmental issues due to over-exploitation of natural resources. This paper analyzes the ecological footprint as a proxy of environmental degradation and determines its influencing factors in 18 MENA countries during 2000–2016. Despite the many studies on the relationship between the ecological footprint and its determinants in the region, the current study use spatial econometric models to take into account spatial dependence in the ecological footprint as well as its determinants. Using a spatial Durbin model, we revealed that neighbors’ behavior can significantly affect a country’s ecological footprint. Factors such as GDP per capita, trade openness, and financial development were found to increase environmental degradation, while the renewable energy consumption, urbanization, and quality of democracy effectively reduce the ecological footprint. These factors not only affect the ecological footprint in the host country, but also affect it in the adjacent countries in different ways. Due to the interdependence of the countries, we recommend development of a regional vision of the bio-economy such that the scope of the analysis goes beyond the country level to account for territorial effects. Furthermore, considering the great potential for renewable energy consumption in the region, we recommend MENA countries to develop use of renewable energy sources in order to reduce environmental degradation in the regio

Global, regional, and national burden of colorectal cancer and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Funding: F Carvalho and E Fernandes acknowledge support from Fundação para a Ciência e a Tecnologia, I.P. (FCT), in the scope of the project UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences UCIBIO and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy i4HB; FCT/MCTES through the project UIDB/50006/2020. J Conde acknowledges the European Research Council Starting Grant (ERC-StG-2019-848325). V M Costa acknowledges the grant SFRH/BHD/110001/2015, received by Portuguese national funds through Fundação para a Ciência e Tecnologia (FCT), IP, under the Norma Transitória DL57/2016/CP1334/CT0006.proofepub_ahead_of_prin

Robust and accurate localization by visual-inertial odometry

© 2018 Dr. Milad RamezaniReal-time, accurate and seamless localization forms the backbone of various location-aware applications such as autonomous navigation. For instance, in a driverless car or in an autonomous robot operating outdoors or indoors, a navigation system is responsible for providing location-related knowledge so that the system can accomplish tasks with respect to this knowledge. Tasks such as finding an address, gripping or tracking an object, making a location-based decision and so on so forth are highly dependent on a localization. Global Navigation Satellite System (GNSS) is a key technology in meeting this demand, however, in indoor or densely built-up areas, GNSS observations are not fully available. Intense efforts have been devoted to identifying a localization approach which could complement GNSS with inertial sensors, laser scanners, cameras, wireless technologies or the integration of these sensors. Among these localization approaches, the use of cameras has gained popularity over recent years due to their availability, affordability and operability. Applications of cameras range from indoor environments to outdoor environments, micro-scale to macro-scale, under-water to outer space. By having a sequence of images taken from a single camera or multiple cameras, it is possible to estimate the location using multiple view geometry. This localization can further be extended by benefiting from additional information acquired from sensors such as Inertial Navigation Systems (INSs), GNSS observations or laser scanner data. Beginning with a discussion of limitations of current techniques, this research aims to improve the performance of existing approaches that employ the integration of cameras with an INS, which is known as Visual-Inertial Odometry (VIO). The outcome is the presentation of two novel VIOs: a stereo-VIO in which a stereo camera is exploited to improve the robustness of localization by simultaneously taking advantage of visual constraints across the stereo images and sequential images taken in time, and an Omnidirectional VIO (OVIO) in which an omnidirectional camera with a field of view larger than 180 degrees is employed to explore a larger scene surrounding the system. This way, features from a larger portion of the environment around the system will contribute to the estimation resulting in more accurate localization. The proposed approaches have been evaluated with photorealistic-synthetic and real-world data. The results demonstrate the superiority of the proposed methods compared to their counterparts