3D Surfel Map-Aided Visual Relocalization with Learned Descriptors
This work addresses camera pose estimation for robotics or AR/VR applications, presenting an incremental improvement by combining surfel maps with learned descriptors.
The paper tackles visual relocalization by integrating geometric information from a 3D surfel map with learned descriptors to estimate 6-DoF camera poses, achieving consistent alignment with the 3D environment in real-world and simulated evaluations.
In this paper, we introduce a method for visual relocalization using the geometric information from a 3D surfel map. A visual database is first built by global indices from the 3D surfel map rendering, which provides associations between image points and 3D surfels. Surfel reprojection constraints are utilized to optimize the keyframe poses and map points in the visual database. A hierarchical camera relocalization algorithm then utilizes the visual database to estimate 6-DoF camera poses. Learned descriptors are further used to improve the performance in challenging cases. We present evaluation under real-world conditions and simulation to show the effectiveness and efficiency of our method, and make the final camera poses consistently well aligned with the 3D environment.