10 research outputs found
Camera Pose Optimization for 3D Mapping
Digital 3D models of environments are of great value in many applications, but the algorithms that build them autonomously are computationally expensive and require a considerable amount of time to perform this task. In this work, we present an active simultaneous localisation and mapping system that optimises the pose of the sensor for the 3D reconstruction of an environment, while a 2D Rapidly-Exploring Random Tree algorithm controls the motion of the mobile platform for the ground exploration strategy. Our objective is to obtain a 3D map comparable to that obtained using a complete 3D approach in a time interval of the same order of magnitude of a 2D exploration algorithm. The optimisation is performed using a ray-tracing technique from a set of candidate poses based on an uncertainty octree built during exploration, whose values are calculated according to where they have been viewed from. The system is tested in diverse simulated environments and compared with two different exploration methods from the literature, one based on 2D and another one that considers the complete 3D space. Experiments show that combining our algorithm with a 2D exploration method, the 3D map obtained is comparable in quality to that obtained with a pure 3D exploration procedure, but demanding less time.This work was supported in part by the Project ‘‘5R-Red Cervera de Tecnologías Robóticas en Fabricación Inteligente,’’ through the
‘‘Centros Tecnológicos de Excelencia Cervera’’ Program funded by the ‘‘Centre for the Development of Industrial Technology (CDTI),’’
under Contract CER-20211007
FRAME: Fast and Robust Autonomous 3D point cloud Map-merging for Egocentric multi-robot exploration
This article presents a 3D point cloud map-merging framework for egocentric
heterogeneous multi-robot exploration, based on overlap detection and
alignment, that is independent of a manual initial guess or prior knowledge of
the robots' poses. The novel proposed solution utilizes state-of-the-art place
recognition learned descriptors, that through the framework's main pipeline,
offer a fast and robust region overlap estimation, hence eliminating the need
for the time-consuming global feature extraction and feature matching process
that is typically used in 3D map integration. The region overlap estimation
provides a homogeneous rigid transform that is applied as an initial condition
in the point cloud registration algorithm Fast-GICP, which provides the final
and refined alignment. The efficacy of the proposed framework is experimentally
evaluated based on multiple field multi-robot exploration missions in
underground environments, where both ground and aerial robots are deployed,
with different sensor configurations.Comment: to be publishe
Contributions to autonomous robust navigation of mobile robots in industrial applications
151 p.Un aspecto en el que las plataformas móviles actuales se quedan atrás en comparación con el punto que se ha alcanzado ya en la industria es la precisión. La cuarta revolución industrial trajo consigo la implantación de maquinaria en la mayor parte de procesos industriales, y una fortaleza de estos es su repetitividad. Los robots móviles autónomos, que son los que ofrecen una mayor flexibilidad, carecen de esta capacidad, principalmente debido al ruido inherente a las lecturas ofrecidas por los sensores y al dinamismo existente en la mayoría de entornos. Por este motivo, gran parte de este trabajo se centra en cuantificar el error cometido por los principales métodos de mapeado y localización de robots móviles,ofreciendo distintas alternativas para la mejora del posicionamiento.Asimismo, las principales fuentes de información con las que los robots móviles son capaces de realizarlas funciones descritas son los sensores exteroceptivos, los cuales miden el entorno y no tanto el estado del propio robot. Por esta misma razón, algunos métodos son muy dependientes del escenario en el que se han desarrollado, y no obtienen los mismos resultados cuando este varía. La mayoría de plataformas móviles generan un mapa que representa el entorno que les rodea, y fundamentan en este muchos de sus cálculos para realizar acciones como navegar. Dicha generación es un proceso que requiere de intervención humana en la mayoría de casos y que tiene una gran repercusión en el posterior funcionamiento del robot. En la última parte del presente trabajo, se propone un método que pretende optimizar este paso para así generar un modelo más rico del entorno sin requerir de tiempo adicional para ello
Distributed scene reconstruction from multiple mobile platforms
Recent research on mobile robotics has produced new designs that provide
house-hold robots with omnidirectional motion. The image sensor embedded
in these devices motivates the application of 3D vision techniques on them
for navigation and mapping purposes. In addition to this, distributed cheapsensing
systems acting as unitary entity have recently been discovered as an
efficient alternative to expensive mobile equipment.
In this work we present an implementation of a visual reconstruction method,
structure from motion (SfM), on a low-budget, omnidirectional mobile platform,
and extend this method to distributed 3D scene reconstruction with
several instances of such a platform.
Our approach overcomes the challenges yielded by the plaform. The unprecedented
levels of noise produced by the image compression typical of
the platform is processed by our feature filtering methods, which ensure
suitable feature matching populations for epipolar geometry estimation by
means of a strict quality-based feature selection. The robust pose estimation
algorithms implemented, along with a novel feature tracking system,
enable our incremental SfM approach to novelly deal with ill-conditioned
inter-image configurations provoked by the omnidirectional motion. The
feature tracking system developed efficiently manages the feature scarcity
produced by noise and outputs quality feature tracks, which allow robust
3D mapping of a given scene even if - due to noise - their length is shorter
than what it is usually assumed for performing stable 3D reconstructions.
The distributed reconstruction from multiple instances of SfM is attained
by applying loop-closing techniques. Our multiple reconstruction system
merges individual 3D structures and resolves the global scale problem with
minimal overlaps, whereas in the literature 3D mapping is obtained by overlapping
stretches of sequences. The performance of this system is demonstrated
in the 2-session case.
The management of noise, the stability against ill-configurations and the
robustness of our SfM system is validated on a number of experiments and
compared with state-of-the-art approaches. Possible future research areas
are also discussed
SYCOPHANT WIRELESS SENSOR NETWORKS TRACKED BY SPARSE MOBILE WIRELESS SENSOR NETWORKS WHILE COOPERATIVELY MAPPING AN AREA
Documentos apresentados no âmbito do reconhecimento de graus e diplomas estrangeiro
Map-merging algorithms for visual slam: Feasibility study and empirical evaluation
Simultaneous localization and mapping, especially the one relying solely on video data (vSLAM), is a challenging problem that has been extensively studied in robotics and computer vision. State-of-the-art vSLAM algorithms are capable of constructing accurate-enough maps that enable a mobile robot to autonomously navigate an unknown environment. In this work, we are interested in an important problem related to vSLAM, i.e. map merging, that might appear in various practically important scenarios, e.g. in a multi-robot coverage scenario. This problem asks whether different vSLAM maps can be merged into a consistent single representation. We examine the existing 2D and 3D map-merging algorithms and conduct an extensive empirical evaluation in realistic simulated environment (Habitat). Both qualitative and quantitative comparison is carried out and the obtained results are reported and analyzed. © Springer Nature Switzerland AG 2020