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research:multisensorslam [2022/07/22 18:23]
Simon Schaefer |
research:multisensorslam [2022/07/26 16:34] (current)
Simon Schaefer |
| ====== Multi-Sensor SLAM ====== | ====== Multi-Sensor SLAM ====== |
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| ===== Keyframe-Based Visual-Inertial Odometry and SLAM Using Nonlinear Optimisation ===== | |
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| {{youtube>TbKEPA2_-m4 }}Here, we fuse inertial measurements with visual measurements: due to the complementary characteristics of these sensing modalities, they have become a popular choice for accurate SLAM in mobile robotics. While historically the problem has been addressed with filtering, advancements in visual estimation suggest that non-linear optimisation offers superior accuracy, while still tractable in complexity thanks to the sparsity of the underlying problem. Taking inspiration from these findings, we formulate a probabilistic cost function that combines reprojection error of landmarks and inertial terms. We ensure real-time operation by limiting the optimisation to a bounded window of keyframes by applying various marginalisation strategies. Keyframes may be spaced in time by arbitrary intervals, while old measurements are still kept as linearised error terms. | |
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| **Current collaborators:** | |
| <memberlist> | <memberlist> |
| <dokuwiki> | <dokuwiki> |
| <filter> | <filter> |
| <grps>^vslam$</grps> | <grps>^multisensorslam$</grps> |
| </filter> | </filter> |
| </dokuwiki> | </dokuwiki> |
| </memberlist> | </memberlist> |
| * Chris Choi (SRL Imperial College London) | * Chris Choi (SRL Imperial College London) |
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| | ===== Keyframe-Based Visual-Inertial Odometry and SLAM Using Nonlinear Optimisation ===== |
| | |
| | {{youtube>TbKEPA2_-m4 }}Here, we fuse inertial measurements with visual measurements: due to the complementary characteristics of these sensing modalities, they have become a popular choice for accurate SLAM in mobile robotics. While historically the problem has been addressed with filtering, advancements in visual estimation suggest that non-linear optimisation offers superior accuracy, while still tractable in complexity thanks to the sparsity of the underlying problem. Taking inspiration from these findings, we formulate a probabilistic cost function that combines reprojection error of landmarks and inertial terms. We ensure real-time operation by limiting the optimisation to a bounded window of keyframes by applying various marginalisation strategies. Keyframes may be spaced in time by arbitrary intervals, while old measurements are still kept as linearised error terms. |
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| **Former collaborators:** | **Former collaborators:** |
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