Integrated Navigation Systems and Techniques for Autonomous Vehicles
Summary
Autonomous vehicles rely on a suite of sensing and estimation techniques to achieve robust, continuous localisation and path planning in diverse environments. Integrated navigation systems typically fuse inertial navigation units, which measure linear and angular motion via accelerometers and gyroscopes, with satellite-based positioning from GNSS to compensate for the drift inherent in standalone inertial solutions. Advanced fusion algorithms, ranging from loosely coupled Kalman filters to tightly coupled multi-sensor frameworks, seamlessly combine satellite signals with inertial data, odometry, visual inputs and lidar point clouds. Recent trends emphasise adaptive and robust filters that adjust to changing dynamics and error distributions, along with deep-learning models—particularly recurrent networks—that predict motion increments during GNSS outages or signal blockages in urban canyons. Simultaneous Localisation and Mapping approaches extend these frameworks by building and updating environmental maps in real time, yielding improved situational awareness for autonomous navigation. Together, these techniques underpin the global effort to deploy safer, more reliable self-driving cars, aerial drones and marine vessels in complex real-world scenarios.
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Integrated Navigation Systems and Techniques for Autonomous Vehicles publication trend
The graph below shows the total number of articles in integrated navigation systems and techniques for autonomous vehicles across all publications each year (not limited to Nature Index journals).
Technical terms
Inertial Navigation System (INS): System that computes position and orientation by integrating accelerometer and gyroscope measurements over time.
Global Navigation Satellite System (GNSS): Satellite constellation that provides globally available positioning and timing information.
Kalman Filter: Recursive estimator combining model predictions and noisy measurements to infer optimal state estimates under Gaussian uncertainty.
Recurrent Neural Network (RNN): Neural network type where connections form directed cycles, enabling the modelling of sequential data and temporal dependencies.
Gated Recurrent Unit (GRU): RNN variant using gating mechanisms to control information flow, improving learning of long-term dependencies.
Long Short-Term Memory (LSTM): Advanced RNN architecture employing input, output and forget gates to mitigate vanishing gradients and retain relevant past information.
References
- A GRU and AKF-Based Hybrid Algorithm for Improving INS/GNSS Navigation Accuracy during GNSS Outage. Remote Sensing (2022).
- Artificial Neural Networks for Navigation Systems: A Review of Recent Research. Applied Sciences (2023).
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