Underwater vehicle navigation improves with better inertial and acoustic filtering
Equivariant Filter Design for Acoustic and Depth Aided Inertial Navigation Systems
Robotics
Summary
Underwater vehicles can't use GPS to figure out where they are, so they rely on sensors that measure movement and depth. The authors found that the usual mathematical methods to combine these sensor readings can be improved by using a special kind of symmetry called Tangent-Group symmetry. This new method of filtering sensor data reduces navigation errors and gives more reliable estimates of the vehicle’s position and movement. They tested this method using computer simulations and real underwater data, showing it works better than existing approaches.
What this means in practice
- •For auv navigation engineers: Improve position and velocity estimation accuracy in underwater vehicles by integrating IMU, DVL, and depth sensors with the new equivariant filter design.
- •For marine robotics developers: Reduce cumulative navigation error in autonomous underwater robots using the proposed filter to maintain better covariance consistency and decrease position drift.
Authors
Arihant Lunawat, Pieter van Goor, Frank Dellaert, Stefan B. Williams
Abstract
Autonomous Underwater Vehicles (AUVs) navigating without GPS typically fuse inertial measurements with acoustic Doppler Velocity Log (DVL) velocities and pressure-derived depth. Posing the navigation state on a Lie group improves accuracy and consistency. However, state-of-the-art filters based on the Invariant Extended Kalman Filter (IEKF) append the Inertial Measurement Unit (IMU) biases as a Euclidean extension, which breaks the group-affine structure required for exact log-linear error dynamics, causing the reported covariance to degrade alongside the estimate. We apply the Tangent-Group (TG) symmetry, which carries the biases within the geometry of the state space, to derive an Equivariant Filter (EqF) for this system, leaving zero linearization error in the navigation states and second-order error only in the biases. We develop an equivariant output model for the DVL, whose update incurs only third-order linearization error, together with a direct pressure output. Monte Carlo simulations benchmark the TG-EqF against a Two-Frame-Group IEKF and a Multiplicative EKF. The TG-EqF reduces error by 18--25\% against both alternatives in each of attitude, velocity, and position. The main benefit is in the covariance it estimates: its Average Normalized Estimation Error Squared (ANEES) stays closer to its nominal value of one than that of the others. Offline analysis on AUV field data corroborates the findings of the simulations, demonstrating reduced position drift.