Multi-robot simultaneous localization and map** (SLAM) is a crucial capability to obtain
timely situational awareness over large areas. Real-world applications demand multi-robot …

Recovering 3D structure and camera motion from images has been a long-standing focus of
computer vision research and is known as Structure-from-Motion (SfM). Solutions to this …

We present 3DRegNet, a novel deep learning architecture for the registration of 3D scans.
Given a set of 3D point correspondences, we build a deep neural network to address the …

We present a novel, end-to-end learnable, multiview 3D point cloud registration algorithm.
Registration of multiple scans typically follows a two-stage pipeline: the initial pairwise …

In this paper, we present a new method for the multiview registration of point cloud. Previous
multiview registration methods rely on exhaustive pairwise registration to construct a …

Outlier-robust estimation involves estimating some parameters (eg, 3D rotations) from data
samples in the presence of outliers, and is typically formulated as a non-convex and non …

Localization and reconstruction are two highly related research areas. Both of them have
developed rapidly in recent years. Apparently, with the help of ground and aerial meta-data …

In this paper, we revisit the rotation averaging problem applied in global Structure-from-
Motion pipelines. We argue that the main problem of current methods is the minimized cost …

Cameras and LiDARs are currently two types of sensors commonly used for 3D map**.
Vision-based methods are susceptible to textureless regions and lighting, and LiDAR-based …

Optimization-based visual-inertial simultaneous localization and map** system (VI-SLAM)
focuses on the establishment of the loss function using both inertial and visual constraints …