1,093 research outputs found
Orientation-Aware 3D SLAM in Alternating Magnetic Field from Powerlines
Identifying new sensing modalities for indoor localization is an interest of research. This paper studies powerline-induced alternating magnetic field (AMF) that fills the indoor space for the orientation-aware three-dimensional (3D) simultaneous localization and mapping (SLAM). While an existing study has adopted a uniaxial AMF sensor for SLAM in a plane surface, the design falls short of addressing the vector field nature of AMF and is therefore susceptible to sensor orientation variations. Moreover, although the higher spatial variability of AMF in comparison with indoor geomagnetism promotes location sensing resolution, extra SLAM algorithm designs are needed to achieve robustness to trajectory deviations from the constructed map. To address the above issues, we design a new triaxial AMF sensor and a new SLAM algorithm that constructs a 3D AMF intensity map regularized and augmented by a Gaussian process. The triaxial sensorâs orientation estimation is free of the error accumulation problem faced by inertial sensing. From extensive evaluation in eight indoor environments, our AMF-based 3D SLAM achieves sub-1m to 3m median localization errors in spaces of up to 500 m2 , sub-2° mean error in orientation sensing, and outperforms the SLAM systems based on Wi-Fi, geomagnetism, and uniaxial AMF by more than 30%
Loop closure detection of visual SLAM based on variational autoencoder
Loop closure detection is an important module for simultaneous localization and mapping (SLAM). Correct detection of loops can reduce the cumulative drift in positioning. Because traditional detection methods rely on handicraft features, false positive detections can occur when the environment changes, resulting in incorrect estimates and an inability to obtain accurate maps. In this research paper, a loop closure detection method based on a variational autoencoder (VAE) is proposed. It is intended to be used as a feature extractor to extract image features through neural networks to replace the handicraft features used in traditional methods. This method extracts a low-dimensional vector as the representation of the image. At the same time, the attention mechanism is added to the network and constraints are added to improve the loss function for better image representation. In the back-end feature matching process, geometric checking is used to filter out the wrong matching for the false positive problem. Finally, through numerical experiments, the proposed method is demonstrated to have a better precision-recall curve than the traditional method of the bag-of-words model and other deep learning methods and is highly robust to environmental changes. In addition, experiments on datasets from three different scenarios also demonstrate that the method can be applied in real-world scenarios and that it has a good performance
A Benchmark Comparison of Visual Place Recognition Techniques for Resource-Constrained Embedded Platforms
Autonomous navigation has become a widely researched area of expertise over the past few years, gaining a massive following due to its necessity in creating a fully autonomous robotic system. Autonomous navigation is an exceedingly difficult task to accomplish in and of itself. Successful navigation relies heavily on the ability to self-localise oneself within a given environment. Without this awareness of oneâs
own location, it is impossible to successfully navigate in an autonomous manner. Since its inception Simultaneous Localization and Mapping (SLAM) has become one of the most widely researched areas of autonomous navigation. SLAM focuses on self-localization within a mapped or un-mapped environment, and constructing or updating the map of oneâs surroundings. Visual Place Recognition (VPR) is an essential part of any SLAM system. VPR relies on visual cues to determine oneâs location within a mapped environment.
This thesis presents two main topics within the field of VPR. First, this thesis presents a benchmark analysis of several popular embedded platforms when performing VPR. The presented benchmark analyses six different VPR techniques
across three different datasets, and investigates accuracy, CPU usage, memory usage, processing time and power consumption. The benchmark demonstrated a clear relationship between platform architecture and the metrics measured, with platforms of the same architecture achieving comparable accuracy and algorithm efficiency.
Additionally, the Raspberry Pi platform was noted as a standout in terms of algorithm efficiency and power consumption.
Secondly, this thesis proposes an evaluation framework intended to provide information about a VPR techniqueâs useability within a real-time application. The approach
makes use of the incoming frame rate of an image stream and the VPR frame rate, the rate at which the technique can perform VPR, to determine how efficient VPR techniques would be in a real-time environment. This evaluation framework determined that CoHOG would be the most effective algorithm to be deployed in a real-time environment as it had the best ratio between computation time and accuracy
BAMF-SLAM: Bundle Adjusted Multi-Fisheye Visual-Inertial SLAM Using Recurrent Field Transforms
In this paper, we present BAMF-SLAM, a novel multi-fisheye visual-inertial
SLAM system that utilizes Bundle Adjustment (BA) and recurrent field transforms
(RFT) to achieve accurate and robust state estimation in challenging scenarios.
First, our system directly operates on raw fisheye images, enabling us to fully
exploit the wide Field-of-View (FoV) of fisheye cameras. Second, to overcome
the low-texture challenge, we explore the tightly-coupled integration of
multi-camera inputs and complementary inertial measurements via a unified
factor graph and jointly optimize the poses and dense depth maps. Third, for
global consistency, the wide FoV of the fisheye camera allows the system to
find more potential loop closures, and powered by the broad convergence basin
of RFT, our system can perform very wide baseline loop closing with little
overlap. Furthermore, we introduce a semi-pose-graph BA method to avoid the
expensive full global BA. By combining relative pose factors with loop closure
factors, the global states can be adjusted efficiently with modest memory
footprint while maintaining high accuracy. Evaluations on TUM-VI, Hilti-Oxford
and Newer College datasets show the superior performance of the proposed system
over prior works. In the Hilti SLAM Challenge 2022, our VIO version achieves
second place. In a subsequent submission, our complete system, including the
global BA backend, outperforms the winning approach.Comment: Accepted to ICRA202
Spectral Sparsification for Communication-Efficient Collaborative Rotation and Translation Estimation
We propose fast and communication-efficient optimization algorithms for
multi-robot rotation averaging and translation estimation problems that arise
from collaborative simultaneous localization and mapping (SLAM),
structure-from-motion (SfM), and camera network localization applications. Our
methods are based on theoretical relations between the Hessians of the
underlying Riemannian optimization problems and the Laplacians of suitably
weighted graphs. We leverage these results to design a collaborative solver in
which robots coordinate with a central server to perform approximate
second-order optimization, by solving a Laplacian system at each iteration.
Crucially, our algorithms permit robots to employ spectral sparsification to
sparsify intermediate dense matrices before communication, and hence provide a
mechanism to trade off accuracy with communication efficiency with provable
guarantees. We perform rigorous theoretical analysis of our methods and prove
that they enjoy (local) linear rate of convergence. Furthermore, we show that
our methods can be combined with graduated non-convexity to achieve
outlier-robust estimation. Extensive experiments on real-world SLAM and SfM
scenarios demonstrate the superior convergence rate and communication
efficiency of our methods.Comment: Revised extended technical report (37 pages, 15 figures, 6 tables
Language-EXtended Indoor SLAM (LEXIS): A Versatile System for Real-time Visual Scene Understanding
Versatile and adaptive semantic understanding would enable autonomous systems
to comprehend and interact with their surroundings. Existing fixed-class models
limit the adaptability of indoor mobile and assistive autonomous systems. In
this work, we introduce LEXIS, a real-time indoor Simultaneous Localization and
Mapping (SLAM) system that harnesses the open-vocabulary nature of Large
Language Models (LLMs) to create a unified approach to scene understanding and
place recognition. The approach first builds a topological SLAM graph of the
environment (using visual-inertial odometry) and embeds Contrastive
Language-Image Pretraining (CLIP) features in the graph nodes. We use this
representation for flexible room classification and segmentation, serving as a
basis for room-centric place recognition. This allows loop closure searches to
be directed towards semantically relevant places. Our proposed system is
evaluated using both public, simulated data and real-world data, covering
office and home environments. It successfully categorizes rooms with varying
layouts and dimensions and outperforms the state-of-the-art (SOTA). For place
recognition and trajectory estimation tasks we achieve equivalent performance
to the SOTA, all also utilizing the same pre-trained model. Lastly, we
demonstrate the system's potential for planning
Active SLAM: A Review On Last Decade
This article presents a comprehensive review of the Active Simultaneous
Localization and Mapping (A-SLAM) research conducted over the past decade. It
explores the formulation, applications, and methodologies employed in A-SLAM,
particularly in trajectory generation and control-action selection, drawing on
concepts from Information Theory (IT) and the Theory of Optimal Experimental
Design (TOED). This review includes both qualitative and quantitative analyses
of various approaches, deployment scenarios, configurations, path-planning
methods, and utility functions within A-SLAM research. Furthermore, this
article introduces a novel analysis of Active Collaborative SLAM (AC-SLAM),
focusing on collaborative aspects within SLAM systems. It includes a thorough
examination of collaborative parameters and approaches, supported by both
qualitative and statistical assessments. This study also identifies limitations
in the existing literature and suggests potential avenues for future research.
This survey serves as a valuable resource for researchers seeking insights into
A-SLAM methods and techniques, offering a current overview of A-SLAM
formulation.Comment: 34 pages, 8 figures, 6 table
Virtual Education Implementation for Children with Autism Spectrum Disorders Amidst the COVID-19 Pandemic
Background: The COVID-19 pandemic led to widespread school closures in the Washington DC metropolitan region, necessitating a shift from traditional in-person education to virtual platforms. The context was significantly influenced by the evolving pandemic and its impact on the region\u27s COVID-19 positive rates, with the Washington DC region facing some of the highest positive rates in 2020. These escalating positive rates posed substantial challenges to planning and implementing in-person learning, primarily driven by concerns for public health and safety. The region\u27s persistently low percentages of in-person learning made it one of the worst in the country regarding the provision of in-person instruction (Burbio, 2023). The challenges and uncertainties related to school closures, phased reopening, and hybrid learning formats significantly impacted children with Autism Spectrum Disorders (ASD), who rely on specialized educational interventions outlined in Individualized Education Plans (IEPs). Caregivers and education providers had to adapt their strategies for implementing educational interventions using technology, yet evidence-informed guidance in implementation practices in virtual education for children with ASD is lacking.
Objective: This study aims to describe contextual complexities surrounding the experiences of caregivers and education providers implementing virtual education for children with ASD in the Washington DC metropolitan region during the COVID-19 pandemic. It explores factors influencing implementation experiences related to the development and implementation of educational interventions, collaboration among IEP team members, impact of educational interventions on IEP goal achievement, and changes in roles and resources during the pandemic.
Methods: A multi-case study design was conducted using qualitative methods. A purposive selection process identified twenty-five participants, comprised of sixteen caregivers and nine education providers. Each participant engaged in an one-hour virtual interview guided by the Consolidated Framework for Implementation Research (CFIR) featuring open-ended questions. Memos captured significant participant comments, and interviews were transcribed, reviewed for accuracy, and analyzed thematically using NVivo.
Results: Thematic data analysis identified thirty-four initial codes grouped into eight subthemes, revealing three overarching themes. Participants encountered significant challenges in transitioning to virtual education, fostering team collaboration, and implementing virtual education effectively. Study findings highlight the critical need for development of: needs assessments, guidelines, and training. This research culminated in the creation of the Model of Translation and Implementation of Virtual Education for children with ASD (MOTIVE-ASD), standing as a robust call to action and a guide for the development of Standards for Virtual Implementation tailored specifically for virtual education programs.
Conclusion: This study provides valuable insights into the development and implementation of virtual education, team collaboration dynamics, IEP goal achievement, and changes in roles and resources during the pandemic. The findings are instrumental for guiding the development of tailored virtual education programs for enhancing the virtual education experience. As virtual education continues to evolve, interested-parties will be equipped with knowledge and resources to navigate educational implementation remotely for children with ASD, in the face of future natural or public health disasters
H-SLAM: Hybrid Direct-Indirect Visual SLAM
The recent success of hybrid methods in monocular odometry has led to many
attempts to generalize the performance gains to hybrid monocular SLAM. However,
most attempts fall short in several respects, with the most prominent issue
being the need for two different map representations (local and global maps),
with each requiring different, computationally expensive, and often redundant
processes to maintain. Moreover, these maps tend to drift with respect to each
other, resulting in contradicting pose and scene estimates, and leading to
catastrophic failure. In this paper, we propose a novel approach that makes use
of descriptor sharing to generate a single inverse depth scene representation.
This representation can be used locally, queried globally to perform loop
closure, and has the ability to re-activate previously observed map points
after redundant points are marginalized from the local map, eliminating the
need for separate and redundant map maintenance processes. The maps generated
by our method exhibit no drift between each other, and can be computed at a
fraction of the computational cost and memory footprint required by other
monocular SLAM systems. Despite the reduced resource requirements, the proposed
approach maintains its robustness and accuracy, delivering performance
comparable to state-of-the-art SLAM methods (e.g., LDSO, ORB-SLAM3) on the
majority of sequences from well-known datasets like EuRoC, KITTI, and TUM VI.
The source code is available at: https://github.com/AUBVRL/fslam_ros_docker
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