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oai:dspace.mit.edu:1721.1/1447392022-08-30T03:38:18Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

Carlone, Luca Mohamoud, Mubarik M. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2022-08-29T16:08:23Z 2022-08-29T16:08:23Z 2022-05 2022-05-27T16:18:23.294Z https://hdl.handle.net/1721.1/144739 One of the challenges faced by researchers in the field of robot localization and mapping is finding a reliable infrastructure to test their ideas. That infrastructure could be a simulation platform, suitable hardware, or a sensor interface. A useful simulation platform needs to capture the dynamics and the sensor modalities that meet the researchers’ needs. A suitable hardware needs to have the capability to navigate, sense the environments, and use onboard computers to run the software it was designed for. A sensor interface allows adapting and testing algorithms on novel sensors. In this research, we develop an essential hardware and software infrastructure for aiding the development and testing of visual-inertial Simultaneous Localization and Mapping (SLAM) systems. SLAM is a fundamental problem in robot navigation and enables constructing or updating a representation (map) of an environment utilizing sensors on board a robot while concurrently using that representation to localize the robot itself. In visual-inertial SLAM the onboard sensors are cameras (monocular or stereo) and an inertial measurement unit (IMU). The contribution of this thesis is threefold. First, we develop a hardware platform consisting of a real drone capable of running state-of-art metric-semantic SLAM; this infrastructure allows us to test advanced SLAM algorithms using real sensors and real robot dynamics. Second, we develop a multi-robot simulation platform that includes dynamically accurate, photo-realistic drones; this platform allows extending our tests to multi-robot SLAM systems. Finally, we develop a new sensor interface; in particular, we integrate and test an omnidirectional stereo frontend in Kimera, an open-source visual-inertial SLAM pipeline. The thesis presents the design, implementation, and testing of each contribution. M.Eng. Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ Software and Hardware Infrastructure for Visual-Inertial SLAM Thesis application/pdf Master Master of Engineering in Electrical Engineering and Computer Science Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="7039120e-7e4d-4f68-9090-dee337a20f79"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>Software and Hardware Infrastructure for Visual-Inertial SLAM</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2022-05</PublicationDate> <Authors> <Author> <DisplayName>Mohamoud, Mubarik M.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://rightsstatements.org/page/InC-EDU/1.0/</License> <Abstract>One of the challenges faced by researchers in the field of robot localization and mapping is finding a reliable infrastructure to test their ideas. That infrastructure could be a simulation platform, suitable hardware, or a sensor interface. A useful simulation platform needs to capture the dynamics and the sensor modalities that meet the researchers’ needs. A suitable hardware needs to have the capability to navigate, sense the environments, and use onboard computers to run the software it was designed for. A sensor interface allows adapting and testing algorithms on novel sensors. In this research, we develop an essential hardware and software infrastructure for aiding the development and testing of visual-inertial Simultaneous Localization and Mapping (SLAM) systems. SLAM is a fundamental problem in robot navigation and enables constructing or updating a representation (map) of an environment utilizing sensors on board a robot while concurrently using that representation to localize the robot itself. In visual-inertial SLAM the onboard sensors are cameras (monocular or stereo) and an inertial measurement unit (IMU). The contribution of this thesis is threefold. First, we develop a hardware platform consisting of a real drone capable of running state-of-art metric-semantic SLAM; this infrastructure allows us to test advanced SLAM algorithms using real sensors and real robot dynamics. Second, we develop a multi-robot simulation platform that includes dynamically accurate, photo-realistic drones; this platform allows extending our tests to multi-robot SLAM systems. Finally, we develop a new sensor interface; in particular, we integrate and test an omnidirectional stereo frontend in Kimera, an open-source visual-inertial SLAM pipeline. The thesis presents the design, implementation, and testing of each contribution.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>