Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Matrix analysis and applied linear algebra
Matrix analysis and applied linear algebra
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Epipolar Constraints for Vision-Aided Inertial Navigation
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Pedestrian Tracking with Shoe-Mounted Inertial Sensors
IEEE Computer Graphics and Applications
Real-time implementation of airborne inertial-SLAM
Robotics and Autonomous Systems
Journal of Field Robotics - Special Issue on Space Robotics, Part III
Aided Navigation: GPS with High Rate Sensors
Aided Navigation: GPS with High Rate Sensors
Observability-based Rules for Designing Consistent EKF SLAM Estimators
International Journal of Robotics Research
Sliding window filter with application to planetary landing
Journal of Field Robotics - Visual Mapping and Navigation Outdoors
Visual-Inertial Sensor Fusion: Localization, Mapping and Sensor-to-Sensor Self-calibration
International Journal of Robotics Research
Visual-inertial navigation, mapping and localization: A scalable real-time causal approach
International Journal of Robotics Research
IEEE Transactions on Robotics
IEEE Transactions on Robotics
Inverse Depth Parametrization for Monocular SLAM
IEEE Transactions on Robotics
FrameSLAM: From Bundle Adjustment to Real-Time Visual Mapping
IEEE Transactions on Robotics
IEEE Transactions on Robotics
IEEE Transactions on Robotics
Camera-IMU-based localization: Observability analysis and consistency improvement
International Journal of Robotics Research
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In this paper, we focus on the problem of motion tracking in unknown environments using visual and inertial sensors. We term this estimation task visual-inertial odometry (VIO), in analogy to the well-known visual-odometry problem. We present a detailed study of extended Kalman filter (EKF)-based VIO algorithms, by comparing both their theoretical properties and empirical performance. We show that an EKF formulation where the state vector comprises a sliding window of poses (the multi-state-constraint Kalman filter (MSCKF)) attains better accuracy, consistency, and computational efficiency than the simultaneous localization and mapping (SLAM) formulation of the EKF, in which the state vector contains the current pose and the features seen by the camera. Moreover, we prove that both types of EKF approaches are inconsistent, due to the way in which Jacobians are computed. Specifically, we show that the observability properties of the EKF's linearized system models do not match those of the underlying system, which causes the filters to underestimate the uncertainty in the state estimates. Based on our analysis, we propose a novel, real-time EKF-based VIO algorithm, which achieves consistent estimation by (i) ensuring the correct observability properties of its linearized system model, and (ii) performing online estimation of the camera-to-inertial measurement unit (IMU) calibration parameters. This algorithm, which we term MSCKF 2.0, is shown to achieve accuracy and consistency higher than even an iterative, sliding-window fixed-lag smoother, in both Monte Carlo simulations and real-world testing.