Ricerc@Sapienza

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 tackle these issues, proposing a novel unified SLAM architecture specifically designed to standardize the SLAM problem and to address heterogeneous sensor configurations.

Nowadays, Nonlinear Least-Squares embodies the foundation of many Robotics and Computer Vision systems. The research community deeply investigated this topic in the last few years, and this resulted in the development of several open-source solvers to approach constantly increasing classes of problems. In this work, we propose a unified methodology to design and develop efficient Least-Squares Optimization algorithms, focusing on the structures and patterns of each specific domain.

Reliable and efficient Visual Place Recognition is
a major building block of modern SLAM systems. Leveraging
on our prior work, in this paper we present a Hamming
Distance embedding Binary Search Tree (HBST) approach
for binary Descriptor Matching and Image Retrieval. HBST
allows for descriptor Search and Insertion in logarithmic time
by exploiting particular properties of binary descriptors. We
support the idea behind our search structure with a thorough
analysis on the exploited descriptor properties and their effects

In this paper we present ProSLAM, a lightweight open-source stereo visual SLAM system designed with simplicity in mind. This work stems from the experience gathered by the authors while teaching SLAM and aims at providing a highly modular system that can be easily implemented and understood. Rather than focusing on the well known mathematical aspects of stereo visual SLAM, we highlight the data structures and the algorithmic aspects required to realize such a system. We implemented ProSLAM using the C++ programming language in combination with a minimal set of standard libraries.

A Simultaneous Localization and Mapping(SLAM) system is a complex program consisting of several interconnected components with different functionalities such as optimization, tracking or loop detection. Whereas the literature addresses in detail how enhancing the algorithmic aspects ofthe individual components improves SLAM performance, the modal aspects, such as when to localize, relocalize or close a loop, are usually left aside.

In this paper we propose an approach to embed multi-dimensional continuous cues in binary feature descriptors used for visual place recognition. The embedding is achieved by extending each feature descriptor with a binary string that encodes a cue and supports the Hamming distance metric. Augmenting the descriptors in such a way has the advantage of being transparent to the procedure used to compare them. We present a concrete application of our methodology, demonstrating the considered type of continuous cue.

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 ). Rather than treating all cases independently, we use a unified formulation that leads to both a compact derivation and a concise implementation.

Registering models is an essential building block of many robotic applications. In case of three-dimensional data, the models to be aligned usually consist of point clouds. In this letter, we propose a formalism to represent in a uniform manner scenes consisting of high-level geometric primitives, including lines and planes. Additionally, we derive both an iterative and a direct method to determine the transformation between heterogeneous scenes (solver). We analyzed the convergence behavior of this solver on synthetic data.

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 Least-Squares (ILS) algorithms such as Gauss-Newton (GN) or Levenberg-Marquardt (LM) are dominant. Common to all these implementations is the influence of the error function used to measure the difference between prediction and observation.

The last few years have seen a growing use of robots to replace humans in dangerous activities, such as inspections, border control, and military operations. In some application areas, as the latter, there is the need to hide strategic information, such as acquired data or relevant positions. This paper presents a vision based system to find encrypted targets in unknown environments by using small-scale robots and visual cryptography. The robots acquire a scene by a standard RGB camera and use a visual cryptography based technique to encrypt the data.