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Found 6 papers, 4 papers with code
Localization Is All You Evaluate: Data Leakage in Online Mapping Datasets and How to Fix It
Specifically, over $80$% of nuScenes and $40$% of Argoverse 2 validation and test samples are less than $5$ m from a training sample.
Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization
In this paper, we propose a new neural network, the Fine-Grained Segmentation Network (FGSN), that can be used to provide image segmentations with a larger number of labels and can be trained in a self-supervised fashion.
A Cross-Season Correspondence Dataset for Robust Semantic Segmentation
We show that adding the correspondences as extra supervision during training improves the segmentation performance of the convolutional neural network, making it more robust to seasonal changes and weather conditions.
Semantic Match Consistency for Long-Term Visual Localization
Robust and accurate visual localization across large appearance variations due to changes in time of day, seasons, or changes of the environment is a challenging problem which is of importance to application areas such as navigation of autonomous robots.
Long-term Visual Localization using Semantically Segmented Images
Robust cross-seasonal localization is one of the major challenges in long-term visual navigation of autonomous vehicles.
Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds.
