# Find your Way by Observing the Sun and Other Semantic Cues

arXiv: https://arxiv.org/abs/1606.07415 · HTML: https://mlsanity.com/paper/1606.07415/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2016.

## Built on by (most cited first, 6 of 6)

- [OrienterNet: Visual Localization in 2D Public Maps with Neural Matching](https://mlsanity.com/paper/2304.02009/) (2023)
- [BEV-Locator: An End-to-end Visual Semantic Localization Network Using Multi-View Images](https://mlsanity.com/paper/2211.14927/) (2022)
- [Repopulating Street Scenes](https://mlsanity.com/paper/2103.16183/) (2021)
- [Image-based Geolocalization by Ground-to-2.5D Map Matching](https://mlsanity.com/paper/2308.05993/) (2023)
- [Animating Street View](https://mlsanity.com/paper/2310.08534/) (2023)
- [Spatio-Temporal Outdoor Lighting Aggregation on Image Sequences using Transformer Networks](https://mlsanity.com/paper/2202.09206/) (2022)

## Abstract (arXiv)

In this paper we present a robust, efficient and affordable approach to self-localization which does not require neither GPS nor knowledge about the appearance of the world. Towards this goal, we utilize freely available cartographic maps and derive a probabilistic model that exploits semantic cues in the form of sun direction, presence of an intersection, road type, speed limit as well as the ego-car trajectory in order to produce very reliable localization results. Our experimental evaluation shows that our approach can localize much faster (in terms of driving time) with less computation and more robustly than competing approaches, which ignore semantic information.