In this article, we present a method for increasing adaptivity of an existing robust estimation algorithm by learning two parameters to better fit the residual distribution. The analyzed method uses these two parameters to calculate weights for iterative reweighted least squares. This adaptive nature of the weights can be helpful in situations where the noise level varies in the measurements. We test our algorithm first on the point cloud registration problem with synthetic datasets and light detection and ranging (LiDAR) odometry with open source real-world datasets. We show that the existing approach needs an additional manual tuning of a residual scale parameter, which our method directly learns from data and has similar or better performance. We further present the idea of decoupling scale and shape parameters to improve the performance of the algorithm. We give detailed analysis of our algorithm along with its comparison with similar well-known algorithms from literature to show the benefits of the proposed approach.
Analysis of Scale-Variant Robust Kernel Optimization for Nonlinear Least-Squares Problems
IEEE Transactions on Aerospace and Electronic Systems ; 59 , 6 ; 7529-7538
2023-12-01
1521308 byte
Article (Journal)
Electronic Resource
English
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