2026-09-19T04:59:07Zhttps://dspace.mit.edu/server/oai/request

oai:dspace.mit.edu:1721.1/1270412026-06-06T01:04:46Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023col_1721.1_131024

John J. Leonard. Baxter, David P.,Nav. E.(David Paul)Massachusetts Institute of Technology. Massachusetts Institute of Technology. Department of Mechanical Engineering. Massachusetts Institute of Technology. Department of Mechanical Engineering 2020-09-03T17:43:40Z 2020-09-03T17:43:40Z 2020 2020 https://hdl.handle.net/1721.1/127041 1191698733 Thesis: Nav. E., Massachusetts Institute of Technology, Department of Mechanical Engineering, May, 2020 Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, May, 2020 Cataloged from the official PDF of thesis. Includes bibliographical references (pages 75-78). In a step towards the level of autonomy seen in humans, this work attempts to emulate a high level and low level approach to world representation and short term adaptation. Specifically, this work demonstrates an implementation of robotic perception that transforms stereo camera and LIDAR sensor data into a sparse map of semantic objects and a locally consistent flexible occupancy grid. This provides a topological representation for grouping objects into higher level classes and a geometric map for traditional planning. Additionally, a reactive dynamic window obstacle avoidance system is shown to quickly plan short term trajectories that avoid both static and dynamic objects while progressing towards a goal. To combine computational efficiency with the robust advantages of multimodal inference, this work uses Semantic Max Mixture factors to approximate multimodal belief in a manner compatible to nonlinear least squares solvers. Experimental results are presented using a RACECAR mobile robot operating in several hallways of MIT, using AprilTags as surrogates for objects in the Semantic Max Mixtures Algorithm. Future work will seek to further integrate the components to create a closed-loop active semantic navigation and mapping algorithm. by David P. Baxter. Nav.E. S.M. Nav.E. Massachusetts Institute of Technology, Department of Mechanical Engineering S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering 78 pages eng Massachusetts Institute of Technology MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582 Mechanical Engineering. Toward robust active semantic SLAM via Max-Mixtures Toward robust active semantic simultaneous localization and mapping via Max-Mixtures Thesis application/pdf 2020-09-03T17:43:38Z Publication Master MechE unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="4bc14890-e104-4641-b373-e4f8b1e54cd8"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Toward robust active semantic SLAM via Max-Mixtures</Title> <Subtitle>Toward robust active semantic simultaneous localization and mapping via Max-Mixtures</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2020</PublicationDate> <Authors> <Author> <DisplayName>Baxter, David P.,Nav. E.(David Paul)Massachusetts Institute of Technology.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Mechanical Engineering.</Keyword> <Abstract>In a step towards the level of autonomy seen in humans, this work attempts to emulate a high level and low level approach to world representation and short term adaptation. Specifically, this work demonstrates an implementation of robotic perception that transforms stereo camera and LIDAR sensor data into a sparse map of semantic objects and a locally consistent flexible occupancy grid. This provides a topological representation for grouping objects into higher level classes and a geometric map for traditional planning. Additionally, a reactive dynamic window obstacle avoidance system is shown to quickly plan short term trajectories that avoid both static and dynamic objects while progressing towards a goal. To combine computational efficiency with the robust advantages of multimodal inference, this work uses Semantic Max Mixture factors to approximate multimodal belief in a manner compatible to nonlinear least squares solvers. Experimental results are presented using a RACECAR mobile robot operating in several hallways of MIT, using AprilTags as surrogates for objects in the Semantic Max Mixtures Algorithm. Future work will seek to further integrate the components to create a closed-loop active semantic navigation and mapping algorithm.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>