In this paper, we introduce a method for visual relocalization using the geometric information from a 3D surfel map.
For the former we contributed our own dataset composed of five indoor scenes where it is unavoidable to capture images corresponding to views that are hard to uniquely identify.
We propose to construct a view graph to excavate the information of the whole given sequence for absolute camera pose estimation.
We present a multimodal camera relocalization framework that captures ambiguities and uncertainties with continuous mixture models defined on the manifold of camera poses.
We conjecture that this is because of the naive approaches to feature space fusion through summation or concatenation which do not take into account the different strengths of each modality.
To address this issue, we approach camera relocalization with a decoupled solution where feature extraction, coordinate regression, and pose estimation are performed separately.
In this paper we tackle the joint learning problem of keyframe detection and visual odometry towards monocular visual SLAM systems.