# Error Decomposition for Hybrid Localization Systems

arXiv: https://arxiv.org/abs/2410.14264 · HTML: https://mlsanity.com/paper/2410.14264/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2024.

## Built on by (most cited first, 3 of 3)

- [Answer Set Networks: Casting Answer Set Programming into Deep Learning](https://mlsanity.com/paper/2412.14814/) (2024)
- [Probabilistic Mission Design for Neuro-Symbolic Unmanned Aircraft Systems](https://mlsanity.com/paper/2501.01439/) (2024)
- [Right in Time: Reactive Reasoning in Regulated Traffic Spaces](https://mlsanity.com/paper/2603.03977/) (2026)

## Abstract (arXiv)

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.