1 resultado para UNMANNED UNDERWATER VEHICLES
em Massachusetts Institute of Technology
Filtro por publicador
- Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España (4)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (4)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (7)
- Aquatic Commons (22)
- Archive of European Integration (1)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (2)
- Biblioteca de Teses e Dissertações da USP (1)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (8)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (2)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (8)
- Boston University Digital Common (1)
- CaltechTHESIS (3)
- Cambridge University Engineering Department Publications Database (83)
- CentAUR: Central Archive University of Reading - UK (24)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (52)
- Cochin University of Science & Technology (CUSAT), India (10)
- CUNY Academic Works (1)
- Deakin Research Online - Australia (64)
- Digital Commons - Michigan Tech (3)
- Digital Commons at Florida International University (4)
- Digital Peer Publishing (2)
- Digital Repository at Iowa State University (1)
- DigitalCommons - The University of Maine Research (2)
- DigitalCommons@The Texas Medical Center (1)
- DigitalCommons@University of Nebraska - Lincoln (1)
- Doria (National Library of Finland DSpace Services) - National Library of Finland, Finland (1)
- DRUM (Digital Repository at the University of Maryland) (6)
- eResearch Archive - Queensland Department of Agriculture; Fisheries and Forestry (2)
- Greenwich Academic Literature Archive - UK (1)
- Helda - Digital Repository of University of Helsinki (2)
- Indian Institute of Science - Bangalore - Índia (45)
- Instituto Politécnico do Porto, Portugal (23)
- Massachusetts Institute of Technology (1)
- Ministerio de Cultura, Spain (4)
- Plymouth Marine Science Electronic Archive (PlyMSEA) (12)
- Portal de Revistas Científicas Complutenses - Espanha (1)
- Publishing Network for Geoscientific & Environmental Data (33)
- QSpace: Queen's University - Canada (1)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (96)
- Queensland University of Technology - ePrints Archive (285)
- RCAAP - Repositório Científico de Acesso Aberto de Portugal (2)
- Repositório Científico do Instituto Politécnico de Lisboa - Portugal (1)
- Repositório digital da Fundação Getúlio Vargas - FGV (2)
- Repositório Institucional da Universidade de Aveiro - Portugal (2)
- Repositório Institucional da Universidade Federal do Rio Grande - FURG (2)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (13)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (1)
- SAPIENTIA - Universidade do Algarve - Portugal (7)
- Savoirs UdeS : plateforme de diffusion de la production intellectuelle de l’Université de Sherbrooke - Canada (1)
- Universidad del Rosario, Colombia (1)
- Universidad Politécnica de Madrid (56)
- Universidade Complutense de Madrid (1)
- Universidade de Lisboa - Repositório Aberto (3)
- Universidade Federal do Pará (1)
- Universidade Federal do Rio Grande do Norte (UFRN) (1)
- Universita di Parma (1)
- Universitat de Girona, Spain (45)
- University of Connecticut - USA (1)
- University of Michigan (2)
- University of Washington (1)
- WestminsterResearch - UK (14)
Resumo:
This thesis develops an approach to the construction of multidimensional stochastic models for intelligent systems exploring an underwater environment. It describes methods for building models by a three- dimensional spatial decomposition of stochastic, multisensor feature vectors. New sensor information is incrementally incorporated into the model by stochastic backprojection. Error and ambiguity are explicitly accounted for by blurring a spatial projection of remote sensor data before incorporation. The stochastic models can be used to derive surface maps or other representations of the environment. The methods are demonstrated on data sets from multibeam bathymetric surveying, towed sidescan bathymetry, towed sidescan acoustic imagery, and high-resolution scanning sonar aboard a remotely operated vehicle.