2007
DOI: 10.1109/robot.2007.364109
|View full text |Cite
|
Sign up to set email alerts
|

Towards simultaneous recognition, localization and mapping for hand-held and wearable cameras

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
41
0
1

Year Published

2007
2007
2022
2022

Publication Types

Select...
5
1
1

Relationship

0
7

Authors

Journals

citations
Cited by 59 publications
(42 citation statements)
references
References 16 publications
0
41
0
1
Order By: Relevance
“…El Department of Engineering Science de la Universidad de Oxford (UK) desarrolló en 2008 un software denominado PTAMM (Parallel Tracking and Multiple Mapping) centrado en tareas de reconocimiento mediante mapeado visual aplicado a dispositivos portables del que se han derivado varias experiencias de realidad aumentada en espacios expositivos (Castle, 2009). Este software guiaba al usuario en el recorrido mediante un indicador virtual que buscaba aquellos elementos que podían visionarse utilizando la realidad aumentada.…”
Section: El Uso De La Realidad Aumentada En Las Guías Multimedia Paraunclassified
“…El Department of Engineering Science de la Universidad de Oxford (UK) desarrolló en 2008 un software denominado PTAMM (Parallel Tracking and Multiple Mapping) centrado en tareas de reconocimiento mediante mapeado visual aplicado a dispositivos portables del que se han derivado varias experiencias de realidad aumentada en espacios expositivos (Castle, 2009). Este software guiaba al usuario en el recorrido mediante un indicador virtual que buscaba aquellos elementos que podían visionarse utilizando la realidad aumentada.…”
Section: El Uso De La Realidad Aumentada En Las Guías Multimedia Paraunclassified
“…In [4] it was shown that the inclusion of recognized object locations improved the quality of the map, and examples of object localization rescuing a failing SLAM process have been observed. However, this cannot be relied upon owing to the varying and relatively long time between object measurements.…”
Section: Delayed Object Insertionmentioning
confidence: 99%
“…In [4] this limitation was removed by using learned appearance models to recognize objects in the scene. As well as permitting graphical augmentation of the recovered map, it was found that incorporating the recognized objects' geometry into the map improved the robustness and accuracy of localization.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…However, the method relies solely on geometric information and thus planes may not correspond to physical scene structure. In [13], Castle et al detect the presence of planar objects for which appearance knowledge has been learned a priori and then use the known geometric structure to allow insertion of the objects into the map. This gives direct relationship to physical structure but at the expense of prior user interaction.…”
Section: Introductionmentioning
confidence: 99%