4 accepted papers
Indoor visual localization is significant for various applications such as autonomous robots, augmented reality, and mixed reality. Recent advances in visual localization have demonstrated their feasibility in large-scale indoor spaces through coarse-to-fine methods that typically employ three steps…
In this paper, we propose a dependable visual kidnap recovery (KR) framework that pinpoints a unique pose in a given 3D map when a device is turned on. For this framework, we first develop indoor-GeM (i-GeM), which is an extension of GeM [1] but considerably more robust than other global descriptors…
This paper proposes a realistic indoor modeling framework for large-scale indoor spaces. The proposed framework reduces the geometric complexity of an indoor model to efficiently represent large-scale environments for image-based rendering (IBR) approaches. For this purpose, the proposed framework r…
In indoor environments, there exists a few distinctive indoor spaces' features (ISFs). However, up to our knowledge, there is no algorithm that fully utilizes ISF for accurate 3-D SLAM. In this letter, we suggest a sensor system that efficiently captures ISF and propose an algorithm framework that a