2024-03-29T01:00:22Zhttps://www.tdx.cat/oai/requestoai:www.tdx.cat:10803/835892022-12-08T19:22:42Zcom_10803_253col_10803_254
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Appearance-based SLAM
SLAM basado en apariencia
SLAM basat en aparença
Feature stability histogram
Histograma de estabilidad de características
Histograma d'estabilitat de característiques
Long-term SLAM
SLAM de largo término
SLAM de llarg termini
Robotics
Robótica
Robòtica
SLAM
Simultaneous Localisation and Mapping
Omnidirectional vision
Visión omnidireccional
Visió omnidireccional
Laser rangefinder
Telémetro
Telèmetre
Appearance-based mapping and localization using feature stability histograms for mobile robot navigation
[Girona] :
Universitat de Girona,
2012
Accés lliure
http://hdl.handle.net/10803/83589
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Bacca Cortés, Eval Bladimir,
autor
1 recurs en línia (137 pàgines)
Tesi
Doctorat
Universitat de Girona. Departament d'Arquitectura i Tecnologia de Computadors
2012
Universitat de Girona. Departament d'Arquitectura i Tecnologia de Computadors
Tesis i dissertacions electròniques
Salvi, Joaquim,
supervisor acadèmic
Cufí i Solé, Xavier,
supervisor acadèmic
TDX
This work proposes an appearance-based SLAM method whose main contribution is the Feature Stability Histogram (FSH). The FSH is built using a voting schema, if the feature is re-observed, it will be promoted; otherwise it progressively decreases its corresponding FSH value. The FSH is based on the human memory model to deal with changing environments and long-term SLAM. This model introduces concepts of Short-Term memory (STM), which retains information long enough to use it, and Long-Term memory (LTM), which retains information for longer periods of time. If the entries in the STM are rehearsed, they become part of the LTM (i.e. they become more stable). However, this work proposes a different memory model, allowing to any input be part of the STM or LTM considering the input strength. The most stable features are only used for SLAM. This innovative feature management approach is able to cope with changing environments, and long-term SLAM.
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