FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W163891190> ?p ?o ?g. }

• W163891190 abstract "Three-dimensional (3D) mapping deals with the problem of building a map of the unknown environments explored by a mobile robot. In contrast to 2D maps, 3D maps contain richer information of the visited places. Besides enabling robot navigation in 3D, a 3D map of the robot surroundings could be of great importance for higher-level robotic tasks, like scene interpretation and object interaction or manipulation, as well as for visualization purposes in general, which are required in surveillance, urban search and rescue, surveying, and others. Hence, the goal of this thesis is to develop a system which is capable of reconstructing the surrounding environment of a mobile robot as a three-dimensional map. Microsoft Kinect camera is a novel sensing sensor that captures dense depth images along with RGB images at high frame rate. Recently, it has dominated the stage of 3D robotic sensing, as it is low-cost, low-power. For this work, it is used as the exteroceptive sensor and obtains 3D point clouds of the surrounding environment. Meanwhile, the wheel odometry of the robot is used to initialize the search for correspondences between different observations. As a single 3D point cloud generated by the Microsoft Kinect sensor is composed of many tens of thousands of data points, it is necessary to compress the raw data to process them efficiently. The method chosen in this work is to use a feature-based representation which simplifies the 3D mapping procedure. The chosen features are planar surfaces and orthogonal corners, which is based on the fact that indoor environments are designed such that walls, ground floors, pillars, and other major parts of the building structures can be modeled as planar surface patches, which are parallel or perpendicular to each other. While orthogonal corners are presented as higher features which are more distinguishable in indoor environment. In this thesis, the main idea is to obtain spatial constraints between pairwise frames by building correspondences between the extracted vertical plane features and corner features. A plane matching algorithm is presented that maximizes the similarity metric between a pair of planes within a search space to determine correspondences between planes. The corner matching result is based on the plane matching results. The estimated spatial constraints form the edges of a pose graph, referred to as graph-based SLAM front-end. In order to build a map, however, a robot must be able to recognize places that it has previously visited. Limitations in sensor processing problem, coupled with environmental ambiguity, make this difficult. In this thesis, we describe a loop closure detection algorithm by compressing point clouds into viewpoint feature histograms, inspired by their strong recognition ability. The estimated roto-translation between detected loop frames is added to the graph representing this newly discovered constraint. Due to the estimation errors, the estimated edges form a non-globally consistent trajectory. With the aid of a linear pose graph optimizing algorithm, the most likely configuration of the robot poses can be estimated given the edges of the graph, referred to as SLAM back-end. Finally, the 3D map is retrieved by attaching each acquired point cloud to the corresponding pose estimate. The approach is validated through different experiments with a mobile robot in an indoor environment" @default.
• W163891190 created "2016-06-24" @default.
• W163891190 creator A5070617258 @default.
• W163891190 date "2013-01-01" @default.
• W163891190 modified "2023-09-23" @default.
• W163891190 title "Plane-based 3D Mapping for Structured Indoor Environment" @default.
• W163891190 cites W1044338 @default.
• W163891190 cites W1187244281 @default.
• W163891190 cites W142685375 @default.
• W163891190 cites W152395192 @default.
• W163891190 cites W1534710825 @default.
• W163891190 cites W1540596182 @default.
• W163891190 cites W1586173270 @default.
• W163891190 cites W1646998587 @default.
• W163891190 cites W1656165940 @default.
• W163891190 cites W1677409904 @default.
• W163891190 cites W1756216659 @default.
• W163891190 cites W1788048519 @default.
• W163891190 cites W1883517952 @default.
• W163891190 cites W1937083578 @default.
• W163891190 cites W1968917201 @default.
• W163891190 cites W1976809435 @default.
• W163891190 cites W1977464512 @default.
• W163891190 cites W1997645979 @default.
• W163891190 cites W2000969939 @default.
• W163891190 cites W2005389775 @default.
• W163891190 cites W2024908906 @default.
• W163891190 cites W2029449100 @default.
• W163891190 cites W2029780423 @default.
• W163891190 cites W2033574012 @default.
• W163891190 cites W2033873359 @default.
• W163891190 cites W2038694110 @default.
• W163891190 cites W2049981393 @default.
• W163891190 cites W2063549868 @default.
• W163891190 cites W2064169939 @default.
• W163891190 cites W2073660060 @default.
• W163891190 cites W2085261163 @default.
• W163891190 cites W2098764590 @default.
• W163891190 cites W2099353192 @default.
• W163891190 cites W2102145772 @default.
• W163891190 cites W2103544971 @default.
• W163891190 cites W2108673707 @default.
• W163891190 cites W2108833445 @default.
• W163891190 cites W2109749443 @default.
• W163891190 cites W2112669647 @default.
• W163891190 cites W2116833168 @default.
• W163891190 cites W2117749802 @default.
• W163891190 cites W2118379283 @default.
• W163891190 cites W2119538218 @default.
• W163891190 cites W2121013842 @default.
• W163891190 cites W2127418874 @default.
• W163891190 cites W2130086590 @default.
• W163891190 cites W2130422193 @default.
• W163891190 cites W2130813905 @default.
• W163891190 cites W2130993165 @default.
• W163891190 cites W2131821152 @default.
• W163891190 cites W2132631693 @default.
• W163891190 cites W2132761823 @default.
• W163891190 cites W2137052305 @default.
• W163891190 cites W2137374159 @default.
• W163891190 cites W2139291719 @default.
• W163891190 cites W2144594079 @default.
• W163891190 cites W2144824356 @default.
• W163891190 cites W2148420507 @default.
• W163891190 cites W2148916113 @default.
• W163891190 cites W2149895934 @default.
• W163891190 cites W2151103935 @default.
• W163891190 cites W2152422184 @default.
• W163891190 cites W2152864241 @default.
• W163891190 cites W2153054365 @default.
• W163891190 cites W2154459632 @default.
• W163891190 cites W2155537009 @default.
• W163891190 cites W2156561799 @default.
• W163891190 cites W2157246496 @default.
• W163891190 cites W2160821342 @default.
• W163891190 cites W2162161345 @default.
• W163891190 cites W2167061621 @default.
• W163891190 cites W2167353731 @default.
• W163891190 cites W2167387804 @default.
• W163891190 cites W2171592532 @default.
• W163891190 cites W2172282149 @default.
• W163891190 cites W2173782939 @default.
• W163891190 cites W2293047233 @default.
• W163891190 cites W2296754596 @default.
• W163891190 cites W250466150 @default.
• W163891190 cites W3210232381 @default.
• W163891190 cites W66589148 @default.
• W163891190 cites W181320694 @default.
• W163891190 doi "https://doi.org/10.6092/polito/porto/2506288" @default.
• W163891190 hasPublicationYear "2013" @default.
• W163891190 type Work @default.
• W163891190 sameAs 163891190 @default.
• W163891190 citedByCount "1" @default.
• W163891190 countsByYear W1638911902014 @default.
• W163891190 crossrefType "journal-article" @default.
• W163891190 hasAuthorship W163891190A5070617258 @default.
• W163891190 hasConcept C111919701 @default.
• W163891190 hasConcept C121684516 @default.
• W163891190 hasConcept C131979681 @default.
• W163891190 hasConcept C154945302 @default.

• next