A Multi-State Constraint Kalman Filter for Vision-aided Inertial Navigation

By Anastasios I. Mourikis, Stergios I. Roumeliotis. Proceedings 2007 IEEE International Conference on Robotics and Automation, 2007

1,949 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/eb14810d07ec1cf984f85d68dfa764570c721ff2

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Citations in the corpus

Visual-Inertial Sensor Fusion: Localization, Mapping and Sensor-to-Sensor Self-calibration (2011, background): “cameras and IMUs make the sensors suitable for use in combination to estimate ego-motion with high fidelity and at a high update rate (Strelow and Singh 2003; Mourikis and Roumeliotis 2007; Kelly et al. 2008).”
A robust and modular multi-sensor fusion approach applied to MAV navigation (2013, methodology): “In [11] GPS and visual measurements where used for different periods of the experiment.”
Kimera: From SLAM to spatial perception with 3D dynamic scene graphs (2021, methodology): “Table 1 compares the Root Mean Squared Error (RMSE) of the Absolute Translation Error (ATE) of KimeraVIO against state-of-the-art open-source VIO pipelines: OKVIS (Leutenegger et al. 2013), MSCKF (Mourikis and Roumeliotis 2007), ROVIO (Bloesch et al. 2015), VINSMono (Qin et al. 2018), and SVO-GTSAM ”
Visual-Inertial Navigation: A Concise Review (2019, methodology): “To this end, a hybrid estimator was proposed in [109] that used the MSCKF to perform real-time local estimation, and triggered global BA on loop-closure detection.”
High-precision, consistent EKF-based visual-inertial odometry (2013, methodology): “Most present-day algorithms in this class are either extended Kalman filter (EKF)-based methods (Mourikis and Roumeliotis, 2007; Jones and Soatto, 2011; Kelly and Sukhatme, 2011), or methods utilizing iterative minimization over a window of states (Konolige and Agrawal, 2008; Dong-Si and Mourikis,…”
GVINS: Tightly Coupled GNSS–Visual–Inertial Fusion for Smooth and Consistent State Estimation (2021, background): “Based on MSCKF, [3] makes improvements on its accuracy and consistency, and [4] aims to overcome its numerical stability issue especially on mobile devices.”
Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry (2018, background): “In contrast to the MSCKF [29], the depths of pixels are explicitly maintained in the state and optimized for.”
Vision-based state estimation for autonomous rotorcraft MAVs in complex environments (2013, background): “The problem of monocular visual-inertial (VINS) state estimation is well studied in the literature [3]–[5].”
Direct visual-inertial odometry with stereo cameras (2016, methodology): “On this dataset, we compare our method with stereo depth estimation to the keypoint-based nonlinear optimization methods presented in [20] (aslam, aslam-mono) and the filtering-based approach in [25] (msckf).”
Keyframe-based visual–inertial odometry using nonlinear optimization (2015, methodology): “The respective computation requires numeric integration; as common in related literature (Mourikis and Roumeliotis, 2007), we applied the classical Runge-Kutta method, in order to obtain discrete time nonlinear state transition equations fd(x k R) and the error state transition matrix Fd(x k R).”
Visual-Inertial Monocular SLAM With Map Reuse (2016, methodology): “This is due to the marginalization of past states to maintain a constant computational cost [1]– [3], [5], [6], or the use of full smoothing [4], [7], with an almost constant complexity in exploration but that can be as expensive as a batch method in the presence of loop closures.”
A Comprehensive Survey of Visual SLAM Algorithms (2022, methodology): “The multi-state constraint Kalman ﬁlter (MSCKF) [62] can be implemented using both monocular and stereo cameras [63].”
PL-VIO: Tightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line Features (2018, positive): “To address this problem, Mourikis and Roumeliotis [21] proposed the multi-state constraint Kalman filter (MSCKF) which marginalizes out the landmark coordinates from the state vector.”
Iterated extended Kalman filter based visual-inertial odometry using direct photometric feedback (2017, positive): “Similarly, Mourikis and Roumeliotis (2007) estimate the trajectory in an IMU-driven ﬁltering framework using the reprojection error of 3D landmarks as measurement updates.”
Optic-Flow Based Control of a 46g Quadrotor (2013, methodology): “error corrections from features tracked multiple frames apart [8], [10].”
Supplementary Material to: IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation (2015, neutral): “A similar null space projection is used in [6] within a Kalman filter architecture, while a factor graph view on Schur complement is given in [2].”