IEEE Robotics and Automation Letters · 2019 · 79 citations · 23 references
EngineeringAutonomous SystemsRobotics EngineeringComputer VisionRobot PerceptionRobot VisionLocalization UncertaintyNavigation SystemsPose EstimationRobot CalibrationRobot LocalizationSensor Fusion For LocalizationInertial NavigationInertial Navigation SystemsObservabilityObservability AnalysisOdometryRobot Pose EstimationTemporal CalibrationVisual-inertial Navigation
In this letter, we perform in-depth observability analysis for both spatial and temporal calibration parameters of an aided inertial navigation system (INS) with global and/or local sensing modalities. In particular, we analytically show that both spatial and temporal calibration parameters are observable if the sensor platform undergoes random motion. More importantly, we identify four degenerate motion primitives that harm the calibration accuracy and thus should be avoided in reality whenever possible. Interestingly, we also prove that these degenerate motions would still hold even in the case where global pose measurements are available. Leveraging a particular multi-state constrained Kalman filter based vision-aided INS with online spatial and temporal calibration, we perform extensively both Monte-Carlo simulations and real-world experiments with the identified degenerate motions to validate our analysis.
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<i>Tracking and Data Association</i>
Yaakov Bar‐Shalom, Thomas Fortmann, Peter G. Cable · The Journal of the Acoustical Society of America · 1990 · 3.1K citations · Full text
Computing, Mathematics, Physics +14
On-Manifold Preintegration for Real-Time Visual--Inertial Odometry
Christian Förster, Luca Carlone, Frank Dellaert et al. · IEEE Transactions on Robotics · 2016 · 1.4K citations · Full text