This tutorial presents the factor graph, a recently introduced estimation framework that is a
generalization of the Kalman filter. An approach for constructing a factor graph, with its …

In recent years, semidefinite relaxations of common optimization problems in robotics have
attracted growing attention due to their ability to provide globally optimal solutions. In many …

In recent years, there has been remarkable progress in the development of so-called
certifiable perception methods, which leverage semidefinite, convex relaxations to find …

We present the first algorithm to efficiently compute certifiably optimal solutions to range-
aided simultaneous localization and mapping (RA-SLAM) problems. Robotic navigation …

We present novel, convex relaxations for rotation and pose estimation problems that can a
posteriori guarantee global optimality for practical measurement noise levels. Some such …

A common approach to localize a mobile robot is by measuring distances to points of known
positions, called anchors. Locating a device from distance measurements is typically posed …

This study introduces a novel framework, G3Reg, for fast and robust global registration of
LiDAR point clouds. In contrast to conventional complex keypoints and descriptors, we …

Differentiable optimization is a powerful new paradigm capable of reconciling model-based
and learning-based approaches in robotics. However, the majority of robotics optimization …

Accurate, efficient, and robust state estimation is more important than ever in robotics as the
variety of platforms and complexity of tasks continue to grow. Historically, discrete-time filters …

In this paper, we present HoloSLAM which is a novel solution to landmark detection issues
in the simultaneous localization and mapping (SLAM) problem in autonomous robot …