“…Small penalization was applied if expected and real orientations belong to adjacent quadrants, large penalization was applied for opposite quadrants and none for same quadrants. Further details of this variation can be found in [69]. This heading penalization, w h , was then included in the state transition probability:…”
Section: Localization In the Object-based Pose Graphmentioning
This thesis is distributed under license "Creative Commons Attribution -Non Commercial -Non Derivatives".To the past, my childhood, to the present, my family, and to the future.
“…Small penalization was applied if expected and real orientations belong to adjacent quadrants, large penalization was applied for opposite quadrants and none for same quadrants. Further details of this variation can be found in [69]. This heading penalization, w h , was then included in the state transition probability:…”
Section: Localization In the Object-based Pose Graphmentioning
This thesis is distributed under license "Creative Commons Attribution -Non Commercial -Non Derivatives".To the past, my childhood, to the present, my family, and to the future.
“…Early work in robot localization was done in a topological map [1], as was early work on localization in a pipe network [2]. Recent work on topological localization incorporates some geometric information [3], and recent work on localization in pipes also uses both metric and topological information [4].…”
Topological localization is advantageous for robots with limited sensing ability in pipe networks, where localization is made difficult if a robot incorrectly executes an action and arrives at an unknown junction. Novel incorporation of measurement of distance travelled is used in a Hidden Markov Model based localization method, which is shown to improve accuracy.
“…Recent work on topological localization adds the challenges of erroneous repeated observations of the environment at a topological map node, inclusion of information assigned to nearby nodes, and failing to make an observation at a node [11], the last of which is especially applicable where a robot has limited sensing ability as is the case in a pipe environment. Use of geometric information on the robot's orientation has been applied using prior knowledge of the orientation between two topological map nodes [12]. Recent work on localization in pipe networks also incorporates both metric and topological information [13], and similar methods have been applied to autonomous road vehicles [14] [15] [16] [17].…”
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