TY - GEN

T1 - Maximum Consensus Localization Using an Objective Function Based on Helmert's Point Error

AU - Axmann, Jeldrik

AU - Zhang, Yimin

AU - Brenner, Claus

PY - 2023

Y1 - 2023

N2 - Ego-localization is a crucial task for autonomous vehicles. On the one hand, it needs to be very accurate, and on the other hand, very robust to provide reliable pose (position and orientation) information, even in challenging environments. Finding the best ego-position is usually tied to optimizing an objective function based on the sensor measurements. The most common approach is to maximize the likelihood, which leads under the assumption of normally distributed random variables to the well-known least squares minimization, often used in conjunction with recursive estimation, e. g. using a Kalman filter. However, least squares minimization is inherently sensitive to outliers, and consequently, more robust loss functions, such as L1 norm or Huber loss have been proposed. Arguably the most robust loss function is the outlier count, also known as maximum consensus optimization, where the outcome is independent of the outlier magnitude. In this paper, we investigate in detail the performance of maximum consensus localization based on LiDAR data. We elaborate on its shortcomings and propose a novel objective function based on Helmert's point error. In an experiment using 3001 measurement epochs, we show that the maximum consensus localization based on the introduced objective function provides superior results with respect to robustness.

AB - Ego-localization is a crucial task for autonomous vehicles. On the one hand, it needs to be very accurate, and on the other hand, very robust to provide reliable pose (position and orientation) information, even in challenging environments. Finding the best ego-position is usually tied to optimizing an objective function based on the sensor measurements. The most common approach is to maximize the likelihood, which leads under the assumption of normally distributed random variables to the well-known least squares minimization, often used in conjunction with recursive estimation, e. g. using a Kalman filter. However, least squares minimization is inherently sensitive to outliers, and consequently, more robust loss functions, such as L1 norm or Huber loss have been proposed. Arguably the most robust loss function is the outlier count, also known as maximum consensus optimization, where the outcome is independent of the outlier magnitude. In this paper, we investigate in detail the performance of maximum consensus localization based on LiDAR data. We elaborate on its shortcomings and propose a novel objective function based on Helmert's point error. In an experiment using 3001 measurement epochs, we show that the maximum consensus localization based on the introduced objective function provides superior results with respect to robustness.

KW - LiDAR

KW - Localization

KW - Maximum consensus

KW - Point cloud registration

KW - Robust estimation

UR - http://www.scopus.com/inward/record.url?scp=85186495219&partnerID=8YFLogxK

U2 - 10.1109/ITSC57777.2023.10422680

DO - 10.1109/ITSC57777.2023.10422680

M3 - Conference contribution

AN - SCOPUS:85186495219

T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

SP - 2302

EP - 2309

BT - 2023 IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 26th IEEE International Conference on Intelligent Transportation Systems, ITSC 2023

Y2 - 24 September 2023 through 28 September 2023

ER -