Building semantic object maps from sparse and noisy 3D data

Günther, Martin; Wiemann, Thomas; Albrecht, Sven; Hertzberg, Joachim

doi:10.1109/iros.2013.6696668

Cited by 18 publications

(18 citation statements)

References 20 publications

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“…In previous publications, we have presented partial results of this case study using 3D laser scanner data [15] and single RGB-D frames [16]. This paper presents an extended version of these conference publications.…”

Section: Contribution Of This Papermentioning

confidence: 95%

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Model-based furniture recognition for building semantic object maps

Günther

Wiemann

Albrecht

et al. 2017

Artificial Intelligence

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Available online xxxx Keywords: Semantic map Incremental mapping Closed-loop mapping Model-based object recognition 3D point cloud CAD model matching OWL-DL ontologyThis paper presents an approach to creating a semantic map of an indoor environment incrementally and in closed loop, based on a series of 3D point clouds captured by a mobile robot using an RGB-D camera. Based on a semantic model about furniture objects (represented in an OWL-DL ontology with rules attached), we generate hypotheses for locations and 6DoF poses of object instances and verify them by matching a geometric model of the object (given as a CAD model) into the point cloud. The result, in addition to the registered point cloud, is a consistent mesh representation of the environment, further enriched by object models corresponding to the detected pieces of furniture. We demonstrate the robustness of our approach against occlusion and aperture limitations of the RGB-D frames, and against differences between the CAD models and the real objects. We evaluate the complete system on two challenging datasets featuring partial visibility and totaling over 800 frames. The results show complementary strengths and weaknesses of processing each frame directly vs. processing the fully registered scene, which accord with intuitive expectations.

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Section: Contribution Of This Papermentioning

confidence: 95%

“…In the last line a shadow disrupted the outer contour and the segmentation broke the table top plane into two clusters. (Figure reproducedfrom[16].) (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.…”

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confidence: 99%

Model-based furniture recognition for building semantic object maps

Günther

Wiemann

Albrecht

et al. 2017

Artificial Intelligence

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“…Objects from a database are recognized and located in [9] where polyhedral CAD object models are recognized in single RGBD images. Similarly, using point clouds, in [10] geometrical primitives are segmented assuming they correspond to scene objects.…”

Section: Related Workmentioning

confidence: 99%

RoboEarth Semantic Mapping: A Cloud Enabled Knowledge-Based Approach

Riazuelo

Tenorth²,

Marco³

et al. 2015

IEEE Trans. Automat. Sci. Eng.

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Abstract-The vision of the RoboEarth project is to design a knowledge-based system to provide web and cloud services that can transform a simple robot into an intelligent one. In this work we describe the RoboEarth semantic mapping system. The semantic map is composed of (1) an ontology to code the concepts and relations in maps and objects, and (2) a SLAM map providing the scene geometry and the object locations with respect to the robot. We propose to ground the terminological knowledge in the robot perceptions by means of the SLAM map of objects. RoboEarth boosts mapping by providing: (1) a subdatabase of object models relevant for the task at hand, obtained by semantic reasoning, which improves recognition by reducing computation and the false positive rate; (2) the sharing of semantic maps between robots, and (3) software as a service to externalize in the cloud the more intensive mapping computations, while meeting the mandatory hard real time constraints of the robot.To demonstrate the RoboEarth cloud mapping system, we investigate two action recipes that embody semantic map building in a simple mobile robot. The first recipe enables semantic map building for a novel environment while exploiting available prior information about the environment. The second recipe searches for a novel object, with the efficiency boosted thanks to the reasoning on a semantically annotated map. Our experimental results demonstrate that by using RoboEarth cloud services, a simple robot can reliably and efficiently build the semantic maps needed to perform its quotidian tasks. In addition, we show the synergetic relation of the SLAM map of objects that grounds the terminological knowledge coded in the ontology.Note to Practitioners-RoboEarth is a cloud-based knowledge base for robots that transforms a simple robot into an intelligent one thanks to the web services provided. As mapping is a mandatory element on most of the robot systems, we focus on the RoboEarth semantic mapping for robot systems, showing the benefits of the combination of SLAM (Simultaneous Localization And Map building), and knowledge-based reasoning. We show the qualities of our system by means of two experiments: (1) building a map of a novel environment boosted by prior information and (2) efficient searching for a novel object thanks to the knowledgebased reasoning techniques. We can conclude that RoboEarth enables the execution of the proposed methods as web and cloud services that enable advanced perception in a simple robot.

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“…At the semantic map level, Gunther et al build large scale 3D maps from RGB-D data which they further augment with recognized objects based on clustered planar regions; finally they refine the map by replacing the objects with their corresponding CAD models [7]. Mason et al describe experiments over extended periods of time in which they build semantic maps for object query as well as change 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014) September 14-18, 2014, Chicago, IL, USA detection, and they focus on objects lying on top of tables and other planar surfaces; the matching of these objects is done on the basis of the overlap between their convex hulls in 2D [8].…”

Section: Related Workmentioning

confidence: 99%

Meta-rooms: Building and maintaining long term spatial models in a dynamic world

Ambruş

Bore

Folkesson

et al. 2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-We present a novel method for re-creating the static structure of cluttered office environments -which we define as the "meta-room" -from multiple observations collected by an autonomous robot equipped with an RGB-D depth camera over extended periods of time. Our method works directly with point clusters by identifying what has changed from one observation to the next, removing the dynamic elements and at the same time adding previously occluded objects to reconstruct the underlying static structure as accurately as possible. The process of constructing the meta-rooms is iterative and it is designed to incorporate new data as it becomes available, as well as to be robust to environment changes. The latest estimate of the meta-room is used to differentiate and extract clusters of dynamic objects from observations. In addition, we present a method for re-identifying the extracted dynamic objects across observations thus mapping their spatial behaviour over extended periods of time.

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Building semantic object maps from sparse and noisy 3D data

Cited by 18 publications

References 20 publications

Model-based furniture recognition for building semantic object maps

Model-based furniture recognition for building semantic object maps

RoboEarth Semantic Mapping: A Cloud Enabled Knowledge-Based Approach

Meta-rooms: Building and maintaining long term spatial models in a dynamic world

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