5 accepted papers
Robots and autonomous systems need to know where they are within a map to navigate effectively. Thus, simultaneous localization and mapping or SLAM is a common building block of robot navigation systems. When building a map via a SLAM system, robots need to re-recognize places to find loop closure a…
Pose-graph optimization (PGO) is a well-known problem in the robotics community. Optimizing a graph means finding the configuration of the nodes that best satisfies the edges. This is generally achieved using iterative approaches that refine a current solution until convergence. Nowadays, iterative
Simultaneous Localization and Mapping (SLAM) is considered a mature research field with numerous applications and publicly available open-source systems. Despite this maturity, existing SLAM systems often rely on ad-hoc implementations or are tailored to predefined sensor setups. In this work, we ta…
In this paper, we propose a pose-landmark graph optimization back-end that supports maps consisting of points, lines or planes. Our back-end allows representing both homogeneous (point-point, line-line, plane-plane) and heterogeneous measurements (point-on-line, point-on-plane, line-on-plane). Rathe…
In this letter, we propose a pose-landmark graph optimization back-end that supports maps consisting of points, lines, or planes. Our back-end allows representing both homogeneous (point-point, line-line, plane-plane) and heterogeneous measurements (point-on-line, point-on-plane, line-on-plane). Rat