1,083 research outputs found
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Keyframe-based monocular SLAM: design, survey, and future directions
Extensive research in the field of monocular SLAM for the past fifteen years
has yielded workable systems that found their way into various applications in
robotics and augmented reality. Although filter-based monocular SLAM systems
were common at some time, the more efficient keyframe-based solutions are
becoming the de facto methodology for building a monocular SLAM system. The
objective of this paper is threefold: first, the paper serves as a guideline
for people seeking to design their own monocular SLAM according to specific
environmental constraints. Second, it presents a survey that covers the various
keyframe-based monocular SLAM systems in the literature, detailing the
components of their implementation, and critically assessing the specific
strategies made in each proposed solution. Third, the paper provides insight
into the direction of future research in this field, to address the major
limitations still facing monocular SLAM; namely, in the issues of illumination
changes, initialization, highly dynamic motion, poorly textured scenes,
repetitive textures, map maintenance, and failure recovery
EGO-TOPO: Environment Affordances from Egocentric Video
First-person video naturally brings the use of a physical environment to the
forefront, since it shows the camera wearer interacting fluidly in a space
based on his intentions. However, current methods largely separate the observed
actions from the persistent space itself. We introduce a model for environment
affordances that is learned directly from egocentric video. The main idea is to
gain a human-centric model of a physical space (such as a kitchen) that
captures (1) the primary spatial zones of interaction and (2) the likely
activities they support. Our approach decomposes a space into a topological map
derived from first-person activity, organizing an ego-video into a series of
visits to the different zones. Further, we show how to link zones across
multiple related environments (e.g., from videos of multiple kitchens) to
obtain a consolidated representation of environment functionality. On
EPIC-Kitchens and EGTEA+, we demonstrate our approach for learning scene
affordances and anticipating future actions in long-form video.Comment: Published in CVPR 2020, project page:
http://vision.cs.utexas.edu/projects/ego-topo
Vision-based Situational Graphs Generating Optimizable 3D Scene Representations
3D scene graphs offer a more efficient representation of the environment by
hierarchically organizing diverse semantic entities and the topological
relationships among them. Fiducial markers, on the other hand, offer a valuable
mechanism for encoding comprehensive information pertaining to environments and
the objects within them. In the context of Visual SLAM (VSLAM), especially when
the reconstructed maps are enriched with practical semantic information, these
markers have the potential to enhance the map by augmenting valuable semantic
information and fostering meaningful connections among the semantic objects. In
this regard, this paper exploits the potential of fiducial markers to
incorporate a VSLAM framework with hierarchical representations that generates
optimizable multi-layered vision-based situational graphs. The framework
comprises a conventional VSLAM system with low-level feature tracking and
mapping capabilities bolstered by the incorporation of a fiducial marker map.
The fiducial markers aid in identifying walls and doors in the environment,
subsequently establishing meaningful associations with high-level entities,
including corridors and rooms. Experimental results are conducted on a
real-world dataset collected using various legged robots and benchmarked
against a Light Detection And Ranging (LiDAR)-based framework (S-Graphs) as the
ground truth. Consequently, our framework not only excels in crafting a richer,
multi-layered hierarchical map of the environment but also shows enhancement in
robot pose accuracy when contrasted with state-of-the-art methodologies.Comment: 7 pages, 6 figures, 2 table
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
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
Exploiting graph structure in Active SLAM
Aplicando análisis provenientes de la teoría de grafos, la teoría espectral de grafos, la exploración de grafos en línea, generamos un sistema de SLAM activo que incluye la planificación de rutas bajo incertidumbre, extracción de grafos topológicos de entornos y SLAM activo \'optimo.En la planificación de trayectorias bajo incertidumbre, incluimos el análisis de la probabilidad de asociación correcta de datos. Reconociendo la naturaleza estocástica de la incertidumbre, demostramos que planificar para minimizar su valor esperado es más fiable que los actuales algoritmos de planificación de trayectorias con incertidumbre.Considerando el entorno como un conjunto de regiones convexas conectadas podemos tratar la exploración robótica como una exploración de grafos en línea. Se garantiza una cobertura total si el robot visita cada región. La mayoría de los métodos para segmentar el entorno están basados en píxeles y no garantizan que las regiones resultantes sean convexas, además pocos son algoritmos incrementales. En base a esto, modificamos un algoritmo basado en contornos en el que el entorno se representa como un conjunto de polígonos que debe segmentarse en un conjunto de polígonos pseudo convexos. El resultado es un algoritmo de segmentación que produjo regiones pseudo-convexas, robustas al ruido, estables y que obtienen un gran rendimiento en los conjuntos de datos de pruebas.La calidad de un algoritmo se puede medir en términos de cuan cercano al óptimo está su rendimiento. Con esta motivación definimos la esencia de la tarea de exploración en SLAM activo donde las únicas variables son la distancia recorrida y la calidad de la reconstrucción. Restringiendo el dominio al grafo que representa el entorno y probando la relación entre la matriz asociada a la exploración y la asociada al grafo subyacente, podemos calcular la ruta de exploración óptima.A diferencia de la mayoría de la literatura en SLAM activo, proponemos que la heurística para la exploración de grafos consiste en atravesar cada arco una vez. Demostramos que el tipo de grafos resultantes tiene un gran rendimiento con respecto a la trayectoria \'optima, con resultados superiores al 97 \% del \'optimo en algunas medidas de calidad.El algoritmo de SLAM activo TIGRE integra el algoritmo de extracción de grafos propuesto con nuestra versión del algoritmo de exploración incremental que atraviesa cada arco una vez. Nuestro algoritmo se basa en una modificación del algoritmo clásico de Tarry para la búsqueda en laberintos que logra el l\'imite inferior en la aproximación para un algoritmo incremental. Probamos nuestro sistema incremental en un escenario de exploración típico y demostramos que logra un rendimiento similar a los métodos fuera de línea y también demostramos que incluso el método \'optimo que visita todos los nodos calculado fuera de línea tiene un peor rendimiento que el nuestro.<br /
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