🤖 AI Summary
This work addresses the issue of cumulative error in learning-based inertial odometry caused by direct regression of absolute position. To mitigate this, the authors propose estimating incremental displacement (Δp) over a 50 ms sliding window and reconstructing the trajectory via numerical integration. The study introduces Kolmogorov–Arnold Networks (KANs) into IMU odometry for the first time, leveraging their learnable B-spline activation functions to effectively suppress long-term error accumulation. Experiments on the EuRoC MAV dataset demonstrate that, compared to conventional multilayer perceptrons (MLPs), KANs reduce cumulative drift error by 44% while using only approximately one-sixth the number of parameters, and exhibit superior stability under both P₅₀ and P₉₀ metrics.
📝 Abstract
In this study, the learning-based inertial odometry problem is investigated using raw IMU measurements obtained from the EuRoC MAV benchmark dataset. Instead of absolute position regression-a formulation that may lead to large constant errors-the models are trained to estimate the incremental displacement (Δp) over a fixed 50 ms sliding window, and the full trajectory is reconstructed through numerical integration. A standard Multi-Layer Perceptron (MLP) is compared with a Kolmogorov-Arnold Network (KAN) equipped with learnable B-spline activations. Although KAN has 6.9 times fewer parameters than MLP (8,444 versus 57,859), it produces a 44% lower error in terms of final cumulative drift on the test trajectory (9.61 m versus 17.23 m). In addition, KAN exhibits more stable behavior in terms of long-term error accumulation, with lower P_50 and P_90 cumulative drift values. These findings indicate that learnable B-spline-based activations have the potential to reduce error accumulation in the inertial odometry problem.