Error Decomposition for Hybrid Localization Systems

TL;DR

This paper introduces the Kappa-Phi method for decomposing errors in hybrid localization systems, enabling targeted improvements and calibration by analyzing individual error sources in complex autonomous vehicle localization.

Contribution

The paper presents a novel error decomposition approach, the Kappa-Phi method, specifically designed for hybrid localization systems combining viewpoint and global references.

Findings

The Kappa-Phi method effectively decomposes localization errors into interpretable components.

The approach improves error prediction and system calibration.

Theoretical analysis and evaluations demonstrate its potential benefits.

Abstract

Future advanced driver assistance systems and autonomous vehicles rely on accurate localization, which can be divided into three classes: a) viewpoint localization about local references (e.g., via vision-based localization), b) absolute localization about a global reference system (e.g., via satellite navigation), and c) hybrid localization, which presents a combination of the former two. Hybrid localization shares characteristics and strengths of both absolute and viewpoint localization. However, new sources of error, such as inaccurate sensor-setup calibration, complement the potential errors of the respective sub-systems. Therefore, this paper introduces a general approach to analyzing error sources in hybrid localization systems. More specifically, we propose the Kappa-Phi method, which allows for the decomposition of localization errors into individual components, i.e., into a sum of parameterized functions of the measured state (e.g., agent kinematics). The error components can then be leveraged to, e.g., improve localization predictions, correct map data, or calibrate sensor setups. Theoretical derivations and evaluations show that the algorithm presents a promising approach to improve hybrid localization and counter the weaknesses of the system's individual components.