HBST: A Hamming Distance embedding Binary Search Tree for Visual Place Recognition

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 Feature descriptors. We support the idea behind our search structure with a thorough analysis on the exploited descriptor properties and their effects on completeness and complexity of search and insertion. To validate our claims we conducted comparative experiments for HBST and several state-of-the-art methods on a broad range of publicly available datasets. HBST is available as a compact open-source C++ header-only library.Comment: Submitted to IEEE Robotics and Automation Letters (RA-L) 2018 with International Conference on Intelligent Robots and Systems (IROS) 2018 option, 8 pages, 10 figure

Adding Cues to Binary Feature Descriptors for Visual Place Recognition

In this paper we propose an approach to embed continuous and selector 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 two concrete applications of our methodology, demonstrating the two considered types of cues. In addition to that, we conducted on these applications a broad quantitative and comparative evaluation covering five benchmark datasets and several state-of-the-art image retrieval approaches in combination with various binary descriptor types.Comment: 8 pages, 8 figures, source: www.gitlab.com/srrg-software/srrg_bench, submitted to ICRA 201

ProSLAM: Graph SLAM from a Programmer's Perspective

In this paper we present ProSLAM, a lightweight stereo visual SLAM system designed with simplicity in mind. Our work stems from the experience gathered by the authors while teaching SLAM to students 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, in this work we highlight the data structures and the algorithmic aspects that one needs to tackle during the design of such a system. We implemented ProSLAM using the C++ programming language in combination with a minimal set of well known used external libraries. In addition to an open source implementation, we provide several code snippets that address the core aspects of our approach directly in this paper. The results of a thorough validation performed on standard benchmark datasets show that our approach achieves accuracy comparable to state of the art methods, while requiring substantially less computational resources.Comment: 8 pages, 8 figure

Multiscale Snapshots: Visual Analysis of Temporal Summaries in Dynamic Graphs

The overview-driven visual analysis of large-scale dynamic graphs poses a major challenge. We propose Multiscale Snapshots, a visual analytics approach to analyze temporal summaries of dynamic graphs at multiple temporal scales. First, we recursively generate temporal summaries to abstract overlapping sequences of graphs into compact snapshots. Second, we apply graph embeddings to the snapshots to learn low-dimensional representations of each sequence of graphs to speed up specific analytical tasks (e.g., similarity search). Third, we visualize the evolving data from a coarse to fine-granular snapshots to semi-automatically analyze temporal states, trends, and outliers. The approach enables to discover similar temporal summaries (e.g., recurring states), reduces the temporal data to speed up automatic analysis, and to explore both structural and temporal properties of a dynamic graph. We demonstrate the usefulness of our approach by a quantitative evaluation and the application to a real-world dataset.Comment: IEEE Transactions on Visualization and Computer Graphics (TVCG), to appea

Towards Interaction Design for Mobile Devices in Collocated Mixed-Focus Collaboration

In collocated collaboration, applied methods and technologies to support the collaboration process mainly comprise either analog paper and pen methods, large display applications or the usage of several laptops. Whereas paper and pen are easy to use, they impair the digital documentation and further editing. Large displays are expensive, stationary, and depend on speci_c environments. Furthermore, laptops build physical barriers between people, which impedes face-to-face communication. This leads to the fact that direct digitization is still not often performed in collocated collaborative scenarios, although it would be useful for further processing or permanent storing of created content.To address advantages of analog media, especially small size and high ubiquity, and eliminate the disadvantages, namely the lack of direct digitization, we aim at applying mobile devices to collocated collaboration. To contribute to the development of future collaboration tools, we derive and propose concrete design goals for applying mobile devices in collocated mixed-focus collaboration

Plug-and-Play SLAM: A Unified SLAM Architecture for Modularity and Ease of Use

Nowadays, SLAM (Simultaneous Localization and Mapping) is considered by the Robotics community to be a mature field. Currently, there are many open-source systems that are able to deliver fast and accurate estimation in typical real-world scenarios. Still, all these systems often provide an ad-hoc implementation that entailed to predefined sensor configurations. In this work, we tackle this issue, proposing a novel SLAM architecture specifically designed to address heterogeneous sensors' configuration and to standardize SLAM solutions. Thanks to its modularity and to specific design patterns, the presented architecture is easy to extend, enhancing code reuse and efficiency. Finally, adopting our solution, we conducted comparative experiments for a variety of sensor configurations, showing competitive results that confirm state-of-the-art performance

Using microlensed quasars to probe the structure of the Milky Way

This paper presents an investigation into the gravitational microlensing of quasars by stars and stellar remnants in the Milky Way. We present predictions for the all-sky microlensing optical depth, time-scale distributions and event rates for future large-area sky surveys. As expected, the total event rate increases rapidly with increasing magnitude limit, reflecting the fact that the number density of quasars is a steep function of magnitude. Surveys such as Pan-STARRS and LSST should be able to detect more than ten events per year, with typical event durations of around one month. Since microlensing of quasar sources suffers from fewer degeneracies than lensing of Milky Way sources, they could be used as a powerful tool for recovering the mass of the lensing object in a robust, often model-independent, manner. As a consequence, for a subset of these events it will be possible to directly `weigh' the star (or stellar remnant) that is causing the lensing signal, either through higher order microlensing effects and/or high-precision astrometric observations of the lens star (using, for example, Gaia or SIM-lite). This means that such events could play a crucial role in stellar astronomy. Given the current operational timelines for Pan-STARRS and LSST, by the end of the decade they could potentially detect up to 100 events. Although this is still too few events to place detailed constraints on Galactic models, consistency checks can be carried out and such samples could lead to exciting and unexpected discoveries.Comment: 11 pages, 8 figures. MNRAS (in press). Minor revisions according to referee's report; mainly presentational issues and clarification of a few items in the discussion; results and conclusions remain unchange

Candidate microlensing events from M31 observations with the Loiano telescope

Microlensing observations towards M31 are a powerful tool for the study of the dark matter population in the form of MACHOs both in the Galaxy and the M31 halos, a still unresolved issue, as well as for the analysis of the characteristics of the M31 luminous populations. In this work we present the second year results of our pixel lensing campaign carried out towards M31 using the 152 cm Cassini telescope in Loiano. We have established an automatic pipeline for the detection and the characterisation of microlensing variations. We have carried out a complete simulation of the experiment and evaluated the expected signal, including an analysis of the efficiency of our pipeline. As a result, we select 1-2 candidate microlensing events (according to different selection criteria). This output is in agreement with the expected rate of M31 self-lensing events. However, the statistics are still too low to draw definitive conclusions on MACHO lensing.Comment: 12 pages, 6 figures, 5 tables - Accepted for publication in The Astrophysical Journa

First Assessment of the Binary Lens OGLE-2015-BLG-0232

We present an analysis of the microlensing event OGLE-2015-BLG-0232. This event is challenging to characterize for two reasons. First, the light curve is not well sampled during the caustic crossing due to the proximity of the full Moon impacting the photometry quality. Moreover, the source brightness is difficult to estimate because this event is blended with a nearby K dwarf star. We found that the light-curve deviations are likely due to a close brown dwarf companion (i.e., s = 0.55 and q = 0.06), but the exact nature of the lens is still unknown. We finally discuss the potential of follow-up observations to estimate the lens mass and distance in the future