Find your Way by Observing the Sun and Other Semantic Cues
Not on the map. Why?
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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jun 2016.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 3.
6 papers.
- 2023OrienterNet: Visual Localization in 2D Public Maps with Neural Matchingcited by 84
- 2022BEV-Locator: An End-to-end Visual Semantic Localization Network Using Multi-View Imagescited by 12
- 2021Repopulating Street Scenescited by 6
- 2023Image-based Geolocalization by Ground-to-2.5D Map Matchingcited by 4
- 2023Animating Street Viewcited by 3
- 2022Spatio-Temporal Outdoor Lighting Aggregation on Image Sequences using Transformer Networkscited by 2
- 2023Animating Street View12 Oct 2023
- 2023Image-based Geolocalization by Ground-to-2.5D Map Matching11 Aug 2023
- 2023OrienterNet: Visual Localization in 2D Public Maps with Neural Matching4 Apr 2023
- 2022BEV-Locator: An End-to-end Visual Semantic Localization Network Using Multi-View Images27 Nov 2022
- 2022Spatio-Temporal Outdoor Lighting Aggregation on Image Sequences using Transformer Networks18 Feb 2022
- 2021Repopulating Street Scenes30 Mar 2021