{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/144739"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/144739","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Software and Hardware Infrastructure for Visual-Inertial SLAM","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_html":"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_has_math":false,"creators":["Mohamoud, Mubarik M."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Carlone, Luca"],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05","date_published":"2022-05","updated_at":"2026-07-22T22:21:31Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"rights_urls":["http://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/144739","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Carlone, Luca"]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Mohamoud, Mubarik M."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-08-29T16:08:23Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-08-29T16:08:23Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-05"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master","Master of Engineering in Electrical Engineering and Computer Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright - Educational Use Permitted","Copyright MIT"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/page/InC-EDU/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/144739"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Software and Hardware Infrastructure for Visual-Inertial SLAM"]}]}],"canonical_facts":{"dc:contributor.advisor":["Carlone, Luca"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Mohamoud, Mubarik M."],"dc:date.accessioned":["2022-08-29T16:08:23Z"],"dc:date.available":["2022-08-29T16:08:23Z"],"dc:date.issued":["2022-05"],"dc:description.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."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/144739"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"dc:rights.uri":["http://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Software and Hardware Infrastructure for Visual-Inertial SLAM"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Engineering in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:21:31Z"}