Graduation Semester and Year
Summer 2025
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Computer Science
Department
Computer Science and Engineering
First Advisor
Manfred Huber
Second Advisor
Farhad Kamangar
Third Advisor
David Levine
Fourth Advisor
William Beksi
Abstract
Service robots are migrating from tightly controlled factory lines into offices, hospitals, and homes, where they must perceive, remember, and act amid people, clutter, and perpetual change. Humans solve this daily by forming compact, task-relevant “cognitive maps”: we sample just enough sensory detail to guide the moment, stitch those snapshots into a sparse topological scaffold, and continuously refine it as we move. Guided by that insight, this dissertation proposes a biologically inspired mapping framework that turns partial RGB-D observations into a hybrid temporal-spatial memory—locally metric for centimeter-scale navigation yet globally topological for room-to-building navigation. The system first distills raw depth images into salience-weighted key-frames and learned point-feature fields, echoing bottom-up attention in human vision. These frames are then woven into a two-layer map that mirrors hippocampal place- and grid-cell organization: a symbolic graph for long-range planning overlaid with either a Point-Based Neural Field or fine-grained 3-D Gaussian primitives for high precision in navigation. Coupled with lightweight visual SLAM, the map supports real-time loop closure, semantic landmarking, and obstacle-aware path planning on resource-constrained hardware. As an attempt to translate principles of human spatial cognition into algorithms that run on today’s embedded processors, this work closes the gap between partial visual perception and actionable temporary spatial memory, bringing service robots a step closer to seamless assistance wherever people live and work.
Keywords
Robotics, 3D Perception, Mapping, Navigation
Disciplines
Controls and Control Theory | Robotics
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Dang, Tuan T., "3D PERCEPTION, MAPPING, AND NAVIGATION FOR MOBILE COBOT" (2025). Computer Science and Engineering Dissertations. 414.
https://mavmatrix.uta.edu/cse_dissertations/414