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Bayesian space conceptualization and place classification for semantic maps in mobile robotics

https://doi.org/10.1016/j.robot.2008.03.005
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Learning Bayesian Belief Network Classifiers: Algorithms and System
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Pattern Classification
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Pattern Classification
inferredcites →https://doi.org/10.1007/978-3-540-27814-6_27openalex-snapshot
Natural Scene Retrieval Based on a Semantic Modeling Step
inferredcites →https://doi.org/10.1016/j.robot.2006.12.003openalex-snapshot
Supervised semantic labeling of places using information extracted from sensor data
inferredcites →https://doi.org/10.1016/j.robot.2006.12.008openalex-snapshot
Cognitive maps for mobile robots—an object based approach
inferredcites →https://doi.org/10.1016/s0004-3702(97)00078-7openalex-snapshot
Learning metric-topological maps for indoor mobile robot navigation
inferredcites →https://doi.org/10.1016/s0921-8890(03)00006-xopenalex-snapshot
Hybrid simultaneous localization and map building: a natural integration of topological and metric
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Slam based on quantities invariant of the robot's configuration
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Knowledge Acquisition Via Incremental Conceptual Clustering
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Bayesian Robot Programming
inferredcites →https://doi.org/10.1023/b:visi.0000029664.99615.94openalex-snapshot
Distinctive Image Features from Scale-Invariant Keypoints
inferredcites →https://doi.org/10.1108/03684920710743466openalex-snapshot
Pattern Recognition and Machine Learning
inferredcites →https://doi.org/10.1109/69.755626openalex-snapshot
Generalization and generalizability measures
inferredcites →https://doi.org/10.1109/70.928558openalex-snapshot
Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization
inferredcites →https://doi.org/10.1109/cvpr.2003.1211479openalex-snapshot
Object class recognition by unsupervised scale-invariant learning
inferredcites →https://doi.org/10.1109/cvpr.2005.16openalex-snapshot
A Bayesian Hierarchical Model for Learning Natural Scene Categories
inferredcites →https://doi.org/10.1109/cvpr.2005.174openalex-snapshot
Hierarchical Part-Based Visual Object Categorization
inferredcites →https://doi.org/10.1109/iccv.2005.137openalex-snapshot
Learning hierarchical models of scenes, objects, and parts
inferredcites →https://doi.org/10.1109/iros.2005.1545511openalex-snapshot
Multi-hierarchical semantic maps for mobile robotics
inferredcites →https://doi.org/10.1109/iros.2007.4399099openalex-snapshot
A bayesian conceptualization of space for mobile robots
inferredcites →https://doi.org/10.1109/robot.1985.1087373openalex-snapshot
Position referencing and consistent world modeling for mobile robots
inferredcites →https://doi.org/10.1109/robot.2000.844734openalex-snapshot
Appearance-based place recognition for topological localization
inferredcites →https://doi.org/10.1117/1.2819119openalex-snapshot
Book Review: Pattern Recognition and Machine Learning
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Data clustering
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Estimating the Dimension of a Model
inferredcites →https://doi.org/10.3929/ethz-a-010039320openalex-snapshot
Cognitive Spatial Representations for Mobile Robots: Perspectives from a User Study
inferredcites →https://doi.org/10.5075/epfl-thesis-2765openalex-snapshot
Feature-based robot navigation in known and unknown environments
inferredcites →https://doi.org/10.5281/zenodo.19712698openalex-snapshot
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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A Review on Bayesian Meta-Learning for Robot Mapping and Navigation
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Making Sense of Indoor Spaces Using Semantic Web Mining and Situated Robot Perception
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Online Semantic Mapping of Urban Environments
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How Do You Help a Robot to Find a Place? A Supervised Learning Paradigm to Semantically Infer about Places
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RNN-LSTM Based Indoor Scene Classification with HoG Features
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