Visual-Inertial Navigation Systems
Summary
Visual-Inertial Navigation Systems integrate visual and inertial sensor data to deliver robust motion estimation and mapping in environments where satellite signals are absent or unreliable. By combining camera imagery—monocular, stereo or event-based—with accelerations and angular rates measured by an inertial measurement unit, these systems mitigate drift and resolve scale ambiguity. Architectures range from filtering-based pipelines, notably multi-state constraint Kalman filters, to optimisation-based back-ends that perform batch or incremental smoothing. Key developments have focused on resilient feature extraction and tracking, advanced IMU preintegration schemes that account for sensor biases and environmental effects, and multi-stage outlier rejection to maintain estimator consistency amid dynamic scenes. Recent strides in tightly coupled frameworks enable simultaneous estimation of pose, velocity, sensor biases and sparse landmarks, delivering centimetre-level accuracy on resource-limited platforms. Visual-Inertial Navigation Systems now underpin navigation solutions for unmanned aerial vehicles, autonomous ground vehicles, augmented reality devices and wearable platforms, highlighting their global impact on precision localisation and mapping.
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Visual-Inertial Navigation Systems publication trend
The graph below shows the total number of articles in visual-inertial navigation systems across all publications each year (not limited to Nature Index journals).
Technical terms
Visual-Inertial Navigation System (VINS): A sensor fusion framework combining camera imagery and inertial measurements to estimate pose and trajectory in real time.
Inertial Measurement Unit (IMU): A device measuring linear acceleration and angular velocity, subject to drift without visual correction.
Simultaneous Localisation and Mapping (SLAM): The process of concurrently constructing a map of an unknown environment and determining the sensor’s position within it.
Multi-State Constraint Kalman Filter (MSCKF): A Kalman filter variant that enforces constraints across a sliding window of poses to improve estimation accuracy.
Optical flow: A computer vision method for estimating the motion of objects between consecutive images, aiding feature tracking and localisation.
References
- A Review of Visual-Inertial Simultaneous Localization and Mapping from Filtering-Based and Optimization-Based Perspectives. Robotics (2018).
- Robust Stereo Visual Inertial Navigation System Based on Multi-Stage Outlier Removal in Dynamic Environments. Sensors (2020).
- Visual-Inertial Odometry with Robust Initialization and Online Scale Estimation. Sensors (2018).
- Improved IMU Preintegration with Gravity Change and Earth Rotation for Optimization-Based GNSS/VINS. Remote Sensing (2020).
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