Robust Self-Tuning Data Association for Geo-Referencing Using Lane Markings
This work addresses localization challenges for autonomous systems in urban and rural environments, but it is incremental as it builds upon existing representations and methods.
The paper tackles the problem of data association ambiguities in localization using aerial imagery-based maps by proposing a robust self-tuning data association method that adapts search areas based on measurement entropy, resulting in considerable improvement over state-of-the-art outlier mitigation methods, especially in outer-urban scenarios.
Localization in aerial imagery-based maps offers many advantages, such as global consistency, geo-referenced maps, and the availability of publicly accessible data. However, the landmarks that can be observed from both aerial imagery and on-board sensors is limited. This leads to ambiguities or aliasing during the data association. Building upon a highly informative representation (that allows efficient data association), this paper presents a complete pipeline for resolving these ambiguities. Its core is a robust self-tuning data association that adapts the search area depending on the entropy of the measurements. Additionally, to smooth the final result, we adjust the information matrix for the associated data as a function of the relative transform produced by the data association process. We evaluate our method on real data from urban and rural scenarios around the city of Karlsruhe in Germany. We compare state-of-the-art outlier mitigation methods with our self-tuning approach, demonstrating a considerable improvement, especially for outer-urban scenarios.