Geometric-Algebra LMS Adaptive Filter and its Application to Rotation Estimation
This work addresses rotation estimation in computer vision and robotics, offering a domain-specific incremental improvement.
The paper introduces a new adaptive filter based on Geometric Algebra (GA-LMS) for estimating rotations in any dimension, showing it can reduce computational cost in 3D point-cloud registration as the number of points increases.
This paper exploits Geometric (Clifford) Algebra (GA) theory in order to devise and introduce a new adaptive filtering strategy. From a least-squares cost function, the gradient is calculated following results from Geometric Calculus (GC), the extension of GA to handle differential and integral calculus. The novel GA least-mean-squares (GA-LMS) adaptive filter, which inherits properties from standard adaptive filters and from GA, is developed to recursively estimate a rotor (multivector), a hypercomplex quantity able to describe rotations in any dimension. The adaptive filter (AF) performance is assessed via a 3D point-clouds registration problem, which contains a rotation estimation step. Calculating the AF computational complexity suggests that it can contribute to reduce the cost of a full-blown 3D registration algorithm, especially when the number of points to be processed grows. Moreover, the employed GA/GC framework allows for easily applying the resulting filter to estimating rotors in higher dimensions.