LiDAR-Based Place Recognition For Autonomous Driving: A Survey

LiDAR-based place recognition (LPR) plays a pivotal role in autonomous driving, which assists Simultaneous Localization and Mapping (SLAM) systems in reducing accumulated errors and achieving reliable localization. However, existing reviews predominantly concentrate on visual place recognition (VPR) methods. Despite the recent remarkable progress in LPR, to the best of our knowledge, there is no dedicated systematic review in this area. This paper bridges the gap by providing a comprehensive review of place recognition methods employing LiDAR sensors, thus facilitating and encouraging further research. We commence by delving into the problem formulation of place recognition, exploring existing challenges, and describing relations to previous surveys. Subsequently, we conduct an in-depth review of related research, which offers detailed classifications, strengths and weaknesses, and architectures. Finally, we summarize existing datasets, commonly used evaluation metrics, and comprehensive evaluation results from various methods on public datasets. This paper can serve as a valuable tutorial for newcomers entering the field of place recognition and for researchers interested in long-term robot localization. We pledge to maintain an up-to-date project on our website https://github.com/ShiPC-AI/LPR-Survey.Comment: 26 pages,13 figures, 5 table

A Data-Driven Optimization Computational Tool Design for Bike-Sharing Station Distribution in Small to Medium-Sized Cities: A Case Study for Cuenca, Ecuador

Faced with heavy vehicular traffic at present, the strategic implementation of Bike Sharing Systems (BSSs) in cities as an alternative means of transport for users is increasingly being adopted. These solutions reduce the environmental burden posed by other means of transportation, decrease costs for citizens, improves people\u27s health due to physical activity, among other advantages. However, aspects such as the definition of bike stations\u27 locations represent a challenge when these solutions are being implemented. Therefore, this paper presents a software tool design that supports a method that defines the location and number of stations within a BSS. Also, the tool uses a data-driven optimization model to establish the location of stations. Finally, a case study carried out in Cuenca - Ecuador, demonstrates the proposal\u27s feasibility, showing a significant concordance with the consulting firmsconsortia results (70-90% of coincidence) at a lower cost

Hypocalcemia-Induced Seizure: Demystifying the Calcium Paradox

Calcium is essential for both neurotransmitter release and muscle contraction. Given these important physiological processes, it seems reasonable to assume that hypocalcemia may lead to reduced neuromuscular excitability. Counterintuitively, however, clinical observation has frequently documented hypocalcemia€™s role in induction of seizures and general excitability processes such as tetany, Chvostek€™s sign, and bronchospasm. The mechanism of this calcium paradox remains elusive, and very few pathophysiological studies have addressed this conundrum. Nevertheless, several studies primarily addressing other biophysical issues have provided some clues. In this review, we analyze the data of these studies and propose an integrative model to explain this hypocalcemic paradox

Direct observation of magnon-phonon coupling in yttrium iron garnet

The magnetic insulator yttrium iron garnet (YIG) with a ferrimagnetic transition temperature of $\sim$560 K has been widely used in microwave and spintronic devices. Anomalous features in the spin Seeback effect (SSE) voltages have been observed in Pt/YIG and attributed to the magnon-phonon coupling. Here we use inelastic neutron scattering to map out low-energy spin waves and acoustic phonons of YIG at 100 K as a function of increasing magnetic field. By comparing the zero and 9.1 T data, we find that instead of splitting and opening up gaps at the spin wave and acoustic phonon dispersion intersecting points, magnon-phonon coupling in YIG enhances the hybridized scattering intensity. These results are different from expectations of conventional spin-lattice coupling, calling for new paradigms to understand the scattering process of magnon-phonon interactions and the resulting magnon-polarons.Comment: 5 pages, 4 figures, PRB in pres

Cross-Modal Information-Guided Network using Contrastive Learning for Point Cloud Registration

The majority of point cloud registration methods currently rely on extracting features from points. However, these methods are limited by their dependence on information obtained from a single modality of points, which can result in deficiencies such as inadequate perception of global features and a lack of texture information. Actually, humans can employ visual information learned from 2D images to comprehend the 3D world. Based on this fact, we present a novel Cross-Modal Information-Guided Network (CMIGNet), which obtains global shape perception through cross-modal information to achieve precise and robust point cloud registration. Specifically, we first incorporate the projected images from the point clouds and fuse the cross-modal features using the attention mechanism. Furthermore, we employ two contrastive learning strategies, namely overlapping contrastive learning and cross-modal contrastive learning. The former focuses on features in overlapping regions, while the latter emphasizes the correspondences between 2D and 3D features. Finally, we propose a mask prediction module to identify keypoints in the point clouds. Extensive experiments on several benchmark datasets demonstrate that our network achieves superior registration performance.Comment: 8 pages, accepted by RAL 202

Compositional Generalization and Decomposition in Neural Program Synthesis

When writing programs, people have the ability to tackle a new complex task by decomposing it into smaller and more familiar subtasks. While it is difficult to measure whether neural program synthesis methods have similar capabilities, what we can measure is whether they compositionally generalize, that is, whether a model that has been trained on the simpler subtasks is subsequently able to solve more complex tasks. In this paper, we focus on measuring the ability of learned program synthesizers to compositionally generalize. We first characterize several different axes along which program synthesis methods would be desired to generalize, e.g., length generalization, or the ability to combine known subroutines in new ways that do not occur in the training data. Based on this characterization, we introduce a benchmark suite of tasks to assess these abilities based on two popular existing datasets, SCAN and RobustFill. Finally, we make first attempts to improve the compositional generalization ability of Transformer models along these axes through novel attention mechanisms that draw inspiration from a human-like decomposition strategy. Empirically, we find our modified Transformer models generally perform better than natural baselines, but the tasks remain challenging.Comment: Published at the Deep Learning for Code (DL4C) Workshop at ICLR 202

Advancing Ubiquitous Collaboration for Telehealth - A Framework to Evaluate Technology-mediated Collaborative Workflow for Telehealth, Hypertension Exam Workflow Study

Healthcare systems are under siege globally regarding technology adoption; the recent pandemic has only magnified the issues. Providers and patients alike look to new enabling technologies to establish real-time connectivity and capability for a growing range of remote telehealth solutions. The migration to new technology is not as seamless as clinicians and patients would like since the new workflows pose new responsibilities and barriers to adoption across the telehealth ecosystem. Technology-mediated workflows (integrated software and personal medical devices) are increasingly important in patient-centered healthcare; software-intense systems will become integral in prescribed treatment plans [1]. My research explored the path to ubiquitous adoption of technology-mediated workflows from historic roots in the CSCW domain to arrive at an expanded method for evaluating collaborative workflows. This new approach for workflow evaluation, the Collaborative Space – Analysis Framework (CS-AF), was then deployed in a telehealth empirical study of a hypertension exam workflow to evaluate the gains and gaps associated with a technology-mediated workflow enhancements. My findings indicate that technology alone is not the solution; rather, it is an integrated approach that establishes “relative advantage” for patients’ in their personal healthcare plans. Results suggest wider use of the CS-AF for future technology-mediated workflow evaluations in telehealth and other technology-rich domains

NaMemo: Enhancing Lecturers' Interpersonal Competence of Remembering Students' Names

Addressing students by their names helps a teacher to start building rapport with students and thus facilitates their classroom participation. However, this basic yet effective skill has become rather challenging for university lecturers, who have to handle large-sized (sometimes exceeding 100) groups in their daily teaching. To enhance lecturers' competence in delivering interpersonal interaction, we developed NaMemo, a real-time name-indicating system based on a dedicated face-recognition pipeline. This paper presents the system design, the pilot feasibility test, and our plan for the following study, which aims to evaluate NaMemo's impacts on learning and teaching, as well as to probe design implications including privacy considerations.Comment: DIS '20 Companio