{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/139099"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/139099","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Belief-Space Planning for Real-World Systems: Efficient SLAM-Based Belief Propagation and Continuous-Time Safety","abstract":"Uncertainty-aware planning has long been a recurring goal in robotics. By enabling autonomous systems to explicitly reason about their own uncertainty, desirable behaviors that increase observability and ensure robust constraint satisfaction arise naturally from high-level optimization specifications. For partially-observable and under-sensed systems in particular, belief-space planning (BSP) provides a natural probabilistic formulation. Despite significant research attention over the years, a few key challenges have prevented the application of BSP to the real-world systems that would stand to benefit the most, such as SLAM-reliant Micro-Aerial Vehicles (MAVs). The most fundamental of these challenges is that of efficiently propagating the state belief, particularly under SLAM-based estimation schemes like Visual-Inertial Odometry (VIO). This thesis describes a structureless and consistent approximation for belief propagation under SLAM, the efficacy of which is demonstrated in the challenging setting of observability-aware planning for VIO. A key attraction of BSP is the ability to specify constraints on the total probability of failure – however, actually encoding these constraints within practical optimization schemes remains a challenge, particularly for physical systems, which evolve continuously in time. General-purpose Monte-Carlo methods can be used to accurately assess failure rates, but these are cumbersome to optimize against, while more convenient “direct” estimates are based on discrete-time simplifications and fail to meaningfully constrain the full continuous-time risk. To address this gap, a novel risk estimate is derived directly in continuous-time, providing a principled, lightweight, and convenient means of ensuring probabilistic safety for real-world systems. Together, these contributions enable online, risk-constrained BSP for a large class of systems of widespread practical interest.","abstract_html":"Uncertainty-aware planning has long been a recurring goal in robotics. By enabling autonomous systems to explicitly reason about their own uncertainty, desirable behaviors that increase observability and ensure robust constraint satisfaction arise naturally from high-level optimization specifications. For partially-observable and under-sensed systems in particular, belief-space planning (BSP) provides a natural probabilistic formulation. Despite significant research attention over the years, a few key challenges have prevented the application of BSP to the real-world systems that would stand to benefit the most, such as SLAM-reliant Micro-Aerial Vehicles (MAVs). The most fundamental of these challenges is that of efficiently propagating the state belief, particularly under SLAM-based estimation schemes like Visual-Inertial Odometry (VIO). This thesis describes a structureless and consistent approximation for belief propagation under SLAM, the efficacy of which is demonstrated in the challenging setting of observability-aware planning for VIO. A key attraction of BSP is the ability to specify constraints on the total probability of failure – however, actually encoding these constraints within practical optimization schemes remains a challenge, particularly for physical systems, which evolve continuously in time. General-purpose Monte-Carlo methods can be used to accurately assess failure rates, but these are cumbersome to optimize against, while more convenient “direct” estimates are based on discrete-time simplifications and fail to meaningfully constrain the full continuous-time risk. To address this gap, a novel risk estimate is derived directly in continuous-time, providing a principled, lightweight, and convenient means of ensuring probabilistic safety for real-world systems. Together, these contributions enable online, risk-constrained BSP for a large class of systems of widespread practical interest.","abstract_has_math":false,"creators":["Frey, Kristoffer M."],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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A key attraction of BSP is the ability to specify constraints on the total probability of failure – however, actually encoding these constraints within practical optimization schemes remains a challenge, particularly for physical systems, which evolve continuously in time. General-purpose Monte-Carlo methods can be used to accurately assess failure rates, but these are cumbersome to optimize against, while more convenient “direct” estimates are based on discrete-time simplifications and fail to meaningfully constrain the full continuous-time risk. To address this gap, a novel risk estimate is derived directly in continuous-time, providing a principled, lightweight, and convenient means of ensuring probabilistic safety for real-world systems. Together, these contributions enable online, risk-constrained BSP for a large class of systems of widespread practical interest."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Belief-Space Planning for Real-World Systems: Efficient SLAM-Based Belief Propagation and Continuous-Time Safety"]}]}],"canonical_facts":{"dc:contributor.advisor":["How, Jonathan P."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Aeronautics and Astronautics"],"dc:creator":["Frey, Kristoffer M."],"dc:date.accessioned":["2022-01-14T14:49:48Z"],"dc:date.available":["2022-01-14T14:49:48Z"],"dc:date.issued":["2021-06"],"dc:description.abstract":["Uncertainty-aware planning has long been a recurring goal in robotics. 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A key attraction of BSP is the ability to specify constraints on the total probability of failure – however, actually encoding these constraints within practical optimization schemes remains a challenge, particularly for physical systems, which evolve continuously in time. General-purpose Monte-Carlo methods can be used to accurately assess failure rates, but these are cumbersome to optimize against, while more convenient “direct” estimates are based on discrete-time simplifications and fail to meaningfully constrain the full continuous-time risk. To address this gap, a novel risk estimate is derived directly in continuous-time, providing a principled, lightweight, and convenient means of ensuring probabilistic safety for real-world systems. 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