Towards Robust Visual Localization in Challenging Conditions
Licentiate thesis, 2019
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.
benchmark
long-term localization
self-driving cars
camera pose estimation
Visual localization
autonomous vehicles
Author
Carl Toft
Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering
Long-term Visual Localization using Semantically Segmented Images
2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA),;(2018)p. 6484-6490
Paper in 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 in proceeding
Long-term 3D Localization and Pose from Semantic Labellings
IEEE International Conference on Computer Vision Workshops,;(2017)p. 650-659
Paper in 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
Subject Categories
Robotics
Computer Science
Computer Vision and Robotics (Autonomous Systems)
Publisher
Chalmers
EB
Opponent: Magnus Oskarsson, Lunds tekniska högskola, Sverige