2026-09-19T13:31:35Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1390992022-01-15T03:04:57Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
How, Jonathan P.
Frey, Kristoffer M.
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
2022-01-14T14:49:48Z
2022-01-14T14:49:48Z
2021-06
2021-06-16T13:26:29.842Z
https://hdl.handle.net/1721.1/139099
0000-0001-6798-1446
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.
Ph.D.
Massachusetts Institute of Technology
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Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
Belief-Space Planning for Real-World Systems: Efficient SLAM-Based Belief Propagation and Continuous-Time Safety
Thesis
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Doctoral
Doctor of Philosophy
Publication
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unknown
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<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>
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