In general, the problems of objects' and rooms' categorizations for robotic applications ... more In general, the problems of objects' and rooms' categorizations for robotic applications have been addressed separately. The current trend is, however, towards a joint modelling of both issues in order to leverage their mutual contextual relations: object → room (e.g. the detection of a microwave indicates that the room is likely to be a kitchen), and room → object (e.g. if the robot is in a bathroom, it is probable to find a toilet). Probabilistic Graphical Models (PGMs) are typically employed to conveniently cope with such relations, relying on inference processes to hypothesize about objects' and rooms' categories. In this work we present a Conditional Random Field (CRF) model, a particular type of PGM, to jointly categorize objects and rooms from RGBD images exploiting object-object and object-room relations. The learning phase of the proposed CRF uses Human Knowledge (HK) to eliminate the necessity of gathering real training data. Concretely, HK is acquired through elicitation and codified into an ontology, which is exploited to effortless generate an arbitrary number of representative synthetic samples for training. The performance of the proposed CRF model has been assessed using the NYU2 dataset, achieving a success of ~ 70% categorizing both, objects and rooms.
In robotics, semantic mapping refers to the construction of a rich representation of the environm... more In robotics, semantic mapping refers to the construction of a rich representation of the environment that includes high level information needed by the robot to accomplish its tasks. Building a semantic map requires algorithms to process sensor data at different levels: geometric, topological and object detections/categories, which must be integrated into an unified model. This paper describes a robotic architecture that successfully builds such semantic maps for indoor environments. For this purpose, within a ROS-based ecosystem, we apply a state-of-the-art Convolutional Neural Network (CNN), concretely YOLOv3, for detecting objects in images. The detection results are placed within a geometric map of the environment making use of a number of modules of the architecture: robot localization, camera extrinsic calibration, data form a depth camera, etc. We demonstrate the suitability of the proposed framework by building semantic maps of several home environments from the Robot@Home dataset, using Unity 3D as a tool to visualize the maps as well as to provide future robotic developments.
This work addresses the fundamental problem of pose graph optimization (PGO), which is pervasive ... more This work addresses the fundamental problem of pose graph optimization (PGO), which is pervasive in the context of SLAM, and widely known as <inline-formula><tex-math notation="LaTeX">$\text{SE}(d)$</tex-math></inline-formula> -synchronization in the mathematical community. Our contribution is twofold. First, we provide a novel, elegant, and compact matrix formulation of the maximum likelihood estimation (MLE) for this problem, drawing interesting connections with the connection Laplacian of a graph object. Second, even though the MLE problem is nonconvex and computationally intractable in general, we exploit recent advances in convex relaxations of PGO and Riemannian techniques for low-rank optimization to yield an <italic>a posteriori certifiably globally optimal</italic> algorithm [A. Bandeira, “A note on probably certifiably correct algorithms,” <italic>Comptes Rendus Mathematique </italic>, vol. 354, pp. 329–333, 2016.] that is also <italic>fast</italic> and <italic>scalable</italic>. This work builds upon a fairly demanding mathematical machinery, but beyond the theoretical basis presented, we demonstrate its performance through extensive experimentation in common large-scale SLAM datasets. The proposed framework, <monospace>Cartan-Sync</monospace>, is up to one order of magnitude faster that the state-of-the-art <monospace>SE-Sync </monospace> [D. M. Rosen <italic>et al.</italic> “A certifiably correct algorithm for synchronization over the special Euclidean group,” in <italic>Proc. Int. Workshop Algorithmic Found. Robot.</italic>, 2016.] in some important scenarios (e.g., the KITTI dataset). We make the code for <monospace>Cartan-Sync</monospace> available at <uri>bitbucket.org/jesusbriales/cartan-sync</uri>, along with some examples and guides for a friendly use by researchers in the field, hoping to promote further adoption and exploitation of these techniques in the robotics community.
In general, the problems of objects' and rooms' categorizations for robotic applications ... more In general, the problems of objects' and rooms' categorizations for robotic applications have been addressed separately. The current trend is, however, towards a joint modelling of both issues in order to leverage their mutual contextual relations: object → room (e.g. the detection of a microwave indicates that the room is likely to be a kitchen), and room → object (e.g. if the robot is in a bathroom, it is probable to find a toilet). Probabilistic Graphical Models (PGMs) are typically employed to conveniently cope with such relations, relying on inference processes to hypothesize about objects' and rooms' categories. In this work we present a Conditional Random Field (CRF) model, a particular type of PGM, to jointly categorize objects and rooms from RGBD images exploiting object-object and object-room relations. The learning phase of the proposed CRF uses Human Knowledge (HK) to eliminate the necessity of gathering real training data. Concretely, HK is acquired through elicitation and codified into an ontology, which is exploited to effortless generate an arbitrary number of representative synthetic samples for training. The performance of the proposed CRF model has been assessed using the NYU2 dataset, achieving a success of ~ 70% categorizing both, objects and rooms.
In robotics, semantic mapping refers to the construction of a rich representation of the environm... more In robotics, semantic mapping refers to the construction of a rich representation of the environment that includes high level information needed by the robot to accomplish its tasks. Building a semantic map requires algorithms to process sensor data at different levels: geometric, topological and object detections/categories, which must be integrated into an unified model. This paper describes a robotic architecture that successfully builds such semantic maps for indoor environments. For this purpose, within a ROS-based ecosystem, we apply a state-of-the-art Convolutional Neural Network (CNN), concretely YOLOv3, for detecting objects in images. The detection results are placed within a geometric map of the environment making use of a number of modules of the architecture: robot localization, camera extrinsic calibration, data form a depth camera, etc. We demonstrate the suitability of the proposed framework by building semantic maps of several home environments from the Robot@Home dataset, using Unity 3D as a tool to visualize the maps as well as to provide future robotic developments.
This work addresses the fundamental problem of pose graph optimization (PGO), which is pervasive ... more This work addresses the fundamental problem of pose graph optimization (PGO), which is pervasive in the context of SLAM, and widely known as <inline-formula><tex-math notation="LaTeX">$\text{SE}(d)$</tex-math></inline-formula> -synchronization in the mathematical community. Our contribution is twofold. First, we provide a novel, elegant, and compact matrix formulation of the maximum likelihood estimation (MLE) for this problem, drawing interesting connections with the connection Laplacian of a graph object. Second, even though the MLE problem is nonconvex and computationally intractable in general, we exploit recent advances in convex relaxations of PGO and Riemannian techniques for low-rank optimization to yield an <italic>a posteriori certifiably globally optimal</italic> algorithm [A. Bandeira, “A note on probably certifiably correct algorithms,” <italic>Comptes Rendus Mathematique </italic>, vol. 354, pp. 329–333, 2016.] that is also <italic>fast</italic> and <italic>scalable</italic>. This work builds upon a fairly demanding mathematical machinery, but beyond the theoretical basis presented, we demonstrate its performance through extensive experimentation in common large-scale SLAM datasets. The proposed framework, <monospace>Cartan-Sync</monospace>, is up to one order of magnitude faster that the state-of-the-art <monospace>SE-Sync </monospace> [D. M. Rosen <italic>et al.</italic> “A certifiably correct algorithm for synchronization over the special Euclidean group,” in <italic>Proc. Int. Workshop Algorithmic Found. Robot.</italic>, 2016.] in some important scenarios (e.g., the KITTI dataset). We make the code for <monospace>Cartan-Sync</monospace> available at <uri>bitbucket.org/jesusbriales/cartan-sync</uri>, along with some examples and guides for a friendly use by researchers in the field, hoping to promote further adoption and exploitation of these techniques in the robotics community.
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