2021 IEEE International Intelligent Transportation Systems Conference (ITSC) | 2021
Error Decomposition for Hybrid Localization Systems
Abstract
Future advanced driver assistance systems and autonomous vehicles rely on accurate localization which can be divided into three classes: a) viewpoint localization with regard to local references (e.g., via vision-based localization), b) absolute localization with regard to 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 analyze 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 as well as evaluations show that the algorithm presents a promising approach to improve hybrid localization and to counter the weaknesses of the individual components of the system.