Towards Robust Visual Localization in Challenging Conditions
Licentiatavhandling, 2019

Visual localization is a fundamental problem in computer vision, with a multitude of applications in robotics, augmented reality and structure-from-motion. The basic problem is to, based on one or more images, figure out the position and orientation of the camera which captured these images relative to some model of the environment. Current visual localization approaches typically work well when the images to be localized are captured under similar conditions compared to those captured during mapping. However, when the environment exhibits large changes in visual appearance, due to e.g. variations in weather, seasons, day-night or viewpoint, the traditional pipelines break down. The reason is that the local image features used are based on low-level pixel-intensity information, which is not invariant to these transformations: when the environment changes, this will cause a different set of keypoints to be detected, and their descriptors will be different, making the long-term visual localization problem a challenging one.

In this thesis, four papers are included, which present work towards solving the problem of long-term visual localization. Three of the articles present ideas for how semantic information may be included to aid in the localization process: one approach relies only on the semantic information for visual localization, another shows how the semantics can be used to detect outlier feature correspondences, while the third presents a sequential localization algorithm which relies on the consistency of the reprojection of a semantic model, instead of traditional features. The final article is a benchmark paper, where we present three new benchmark datasets aimed at evaluating localization algorithms in the context of long-term visual localization.


long-term localization

self-driving cars

camera pose estimation

Visual localization

autonomous vehicles

Opponent: Magnus Oskarsson, Lunds tekniska högskola, Sverige


Carl Toft

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Long-term Visual Localization using Semantically Segmented Images


Paper i proceeding

Semantic Match Consistency for Long-Term Visual Localization

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),; Vol. 11206 LNCS(2018)p. 391-408

Paper i proceeding

Long-term 3D Localization and Pose from Semantic Labellings

IEEE International Conference on Computer Vision Workshops,; (2017)p. 650-659

Paper i proceeding

Toft, C., Sattler, T., Maddern, W., Torii, A., Hammarstrand, L., Stenborg, E., Safari, D., Okutomi, M., Pollefeys, M., Sivic, J., Pajdla, T., Kahl, F. Benchmarking 6DoF Outdoor Visual Localization in Changing Conditions


Robotteknik och automation

Datavetenskap (datalogi)

Datorseende och robotik (autonoma system)




Opponent: Magnus Oskarsson, Lunds tekniska högskola, Sverige

Mer information

Senast uppdaterat