8 accepted papers
Finding the relative pose between two calibrated views ranks among the most fundamental geometric vision problems. It therefore appears as somewhat a surprise that a globally optimal solver that minimizes a properly defined energy over non-minimal correspondence sets and in the original space of rel…
This work addresses the fundamental problem of pose graph optimization (PGO), which is pervasive in the context of SLAM, and widely known as SE(d)-synchronization in the mathematical community. Our contribution is twofold. First, we provide a novel, elegant, and compact matrix formulation of the max
The registration of 3D models by a Euclidean transformation is a fundamental task at the core of many application in computer vision. This problem is non-convex due to the presence of rotational constraints, making traditional local optimization methods prone to getting stuck in local minima. This p…
Pose Graph Optimization (PGO) is the de facto choice to solve the trajectory of an agent in Simultaneous Localization and Mapping (SLAM). The Maximum Likelihood Estimation (MLE) for PGO is a non-convex problem for which no known technique is able to guarantee a globally optimal solution under genera…
Graph-based SLAM has proved to be one of the most effective solutions to the Simultaneous Localization and Mapping problem. This approach relies on nonlinear iterative optimization methods that in practice perform both accurately and efficiently. However, due to the non-convexity of the problem, the…
Most approaches to visual odometry estimates the camera motion based on point features, consequently, their performance deteriorates in low-textured scenes where it is difficult to find a reliable set of them. This paper extends a popular semi-direct approach to monocular visual odometry known as SV…
Robots are often equipped with 2D laser-rangefinders (LRFs) and cameras since they complement well to each other. In order to correctly combine the measurements from both sensors, it is required to know their relative pose, that is, to solve their extrinsic calibration. In this paper we present a si…
Robots are often equipped with 2D laser-rangefinders (LRFs) and cameras since they complement well to each other. In order to correctly combine measurements from both sensors, it is required to know their relative pose, that is, to solve their extrinsic calibration. In this paper we present a new ap…