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Bernhard Zeisl

5 accepted papers

2018

LandmarkBoost: Efficient visualContext Classifiers for Robust Localization

IROS 2018poster

The growing popularity of autonomous systems creates a need for reliable and efficient metric pose retrieval algorithms. Currently used approaches tend to rely on nearest neighbor search of binary descriptors to perform the 2D-3D matching and guarantee realtime capabilities on mobile platforms. Thes…

Cited by 9SourceScholar
2017

Efficient descriptor learning for large scale localization

ICRA 2017poster

Many robotics and Augmented Reality (AR) systems that use sparse keypoint-based visual maps operate in large and highly repetitive environments, where pose tracking and localization are challenging tasks. Additionally, these systems usually face further challenges, such as limited computational powe…

Cited by 21SourceScholar