SLAM

In this paper, we present a duo-graph Simultaneous Localization and Mapping (SLAM) method that enables visualguided robot to efficiently and robustly generate consistent 3D map in a large-scale environment. Recently the conditional independent graph (CI-GRAPH) has been proved to be an efficient and robust method for solving the large-scale SLAM problem. However, it stems from extended kalman filter (EKF) SLAM that suffers from high computational load when number of the landmarks becomes large. Moreover, the big noise in the measurement can lead EKF SLAM far from convergence. These disadvantages are the cases in the visual-guided robot.

Στα πλαίσια της παρούσας εργασίας χρησιμοποιήθηκε ο Carla simulator, με βάση τον οποίο δημιουργήθηκαν αυτόνομες συμπεριφορές που εφαρμόζονται σε αυτοκινούμενα αυτοκίνητα, με την γλώσσα Python σε σύστημα με AMD Ryzen 5 με 16 BG RAM και κάρτα γραφικών NVIDIA GeForce GTX 1650 με αυτόνομη RAM 4GB σε Windows.
Χρησιμοποιήθηκαν διάφορες εκδόσεις του simulator – η stable 0.8.2 (που μετράει αρκετά έτη πια και τις λείπουν αρκετά χαρακτηριστικά), η 0.9.10, η 0.9.5 – στην οποία υλοποιήθηκε και έτρεξε μέρος του κώδικα, και η 0.9.8 (στην οποία έτρεξαν τα περισσότερα ερωτήματα).

Mapping and surveying cultural heritage sites is critical for scientific study and conservation; however, existing equipment and methodologies are typically time-consuming, expensive, and non-scalable. We describe the use of Zebedee, a handheld lidar mapping system capable of efficiently producing kilometre-scale 3D maps of general environments in the time it takes to walk through a site. The captured data contains millions of measurements at sufficient accuracy and density to analyse structural and architectural details of the site. We describe the application of Zebedee to map the former site of the Peel Island Lazaret in Queensland, Australia. The device captured data sufficient to generate a detailed 3D model of this remote site over a few hours of operation. The results demonstrate the flexibility and efficiency of the Zebedee system, and suggest its potential application in conservation management and interpretation of cultural heritage.

This paper presents new developments on drone‐based automated survey for the detection of individual items or fragments of material culture visible on the ground surface. Since the publication of our original proof of concept, awarded with the Journal of Archaeological Science and Society for Archaeological Sciences Emerging Investigator Award 2019, additional funding has allowed us to implement a series of improvements to the method. These aim to improve detection capabilities and the extraction of items' shapes and increase flight autonomy, control, area covered per flight and the type of environments in which the method can be applied while reducing computing needs, processing time and expertise necessary for its application. This paper provides an account of the methods followed to achieve these objectives, their preliminary results and the current development for their implementation into a free and open‐source system that can be used by the archaeological community at large.

This paper explores the problem of implementing a teleoperated navigation system on a mobile robot using low cost equipment by critically analysing the current trends in mobile robotics. In the hobbyists robotics community there is a current lack of teleoperated robots capable of simultaneously mapping and localising themselves within an environment. The work presented here builds upon the research of a past student at the Institute of Technology Blanchardstown who set out to create such a robot. In order to address that issue this paper looks at tracking a mobile
robot's position within its environment using commodity mouse sensors and electro magnetic compasses. It then goes on to explore how a simple microcomputer can aid a mobile robot in constructing a consistent map of its environment using a mapping algorithm.

Ability to explore unknown environments and ability to successfully navigate in these environments are two key abilities of any truly autonomous mobile robot. In this article, an efficient implementation of mapping and navigation using only RGBD cameras is presented. The map created by Visual SLAM techniques is converted into Octree structure, and successful A*Navigation between two points in 3D is performed. The created mapping system is able to operate in real-time even on small mobile robot and can be easily extended with new state-of-the art techniques. Proposed way of octree encoding, due to its minimum storage requirements, enables efficient distribution of map between multiple robots over the network. Presented path-planning method with its optimizations runs in sufficient time even in higher resolution maps.

—This paper describes a system for occupancy grid mapping using an autonomous mobile robot equipped with laser range finder. The approach depicted in this article is the occupancy grid mapping with inverse range sensor model. A complex real time system is developed that exchange and process data between the NI robotics starter kit and an external host computer through TCP layer. All programs are developed using labVIEW. Although, the results were not as expected, the resulting maps can give an approximation of the environment of navigation. The inaccuracy of the maps is explained by the reliance on the odometry data for localization, which makes clear the necessity of simultaneous localization and mapping techniques in the field of autonomous navigation. Keywords—Occupancy grid mapping; inverse range sensor model; laser range finder; NI robotics starter kit

The most common way to deal with the uncertainty present in noisy sensorial perception and action is to model the problem with a probabilistic framework. Maximum likelihood estimation is a well-known estimation method used in many robotic and computer vision applications. Under Gaussian assumption, the maximum likelihood estimation converts to a nonlinear least squares problem. Efficient solutions to nonlinear least squares exist and they are based on iteratively solving sparse linear systems until convergence. In general, the existing solutions provide only an estimation of the mean state vector, the resulting covariance being computationally too expensive to recover. Nevertheless, in many simultaneous localization and mapping (SLAM) applications, knowing only the mean vector is not enough. Data association, obtaining reduced state representations, active decisions and next best view are only a few of the applications that require fast state covariance recovery. Furthermore, computer vision and robotic applications are in general performed online. In this case, the state is updated and recomputed every step and its size is continuously growing, therefore, the estimation process may become highly computationally demanding. This paper introduces a general framework for incremental maximum likelihood estimation called SLAM++, which fully benefits from the incremental nature of the online applications, and provides efficient estimation of both the mean and the covariance of the estimate. Based on that, we propose a strategy for maintaining a sparse and scalable state representation for large scale mapping, which uses information theory measures to integrate only informative and non-redundant contributions to the state representation. SLAM++ differs from existing implementations by performing all the matrix operations by blocks. This led to extremely fast matrix manipulation and arithmetic operations used in nonlinear least squares. Even though this paper tests SLAM++ efficiency on SLAM problems, its applicability remains general.

This work presents a mobile robot capable of producing a rough map of an interior of a building. The goal is to build a mobile robot, with the ability of self-localization, navigation, and mapping. To accomplish this, several technologies were used: Arduino development board, the Robot Operating System (ROS), and a Kinect sensor. The constructed robot offers a wide field of applications; besides mapping, other interesting alternatives are the exploration of dangerous zones, telepresence and education.

In 2011, the emergence of "slam", designating drug injection among gay men in a sexual context was taken up by health stakeholders. As slam is rarely documented, a qualitative research was carried out by AIDES, a French HIV/Aids community-based association, in collaboration with other health institutions (Sidaction, the gay doctors association and the Health and medical research national institute). The objective was to take stock of this phenomenon regarding definitions, products used, effects, connection with sexuality, harm reduction strategies implemented as well as prevention and medical needs as declared by slam practitioners.
In 2012, a multidisciplinary team (an anthropologist, two sociologists, a dermato-venereologist, a health psychologist and an insider) conducted a rapid assessment process (RAP). This ethnographic approach insists on the role of the insider, a gay man practicing slam, who ensures the appropriateness of questions and facilitates the rapport researchers-interviewees. The team worked collectively during the whole process (constructing the interview guide, conducting the interviews, analysis and report writing). 23 people from different cities of France were interviewed 17 current and ex- slammers and 6 key informants (clinicians, addictologists and members of NGOs) having an expertise on drug use and gay sexuality.
Slam was defined as the injection of products (méphédrone and derivatives, crystal, cocaine) in a sexual context. This sexual context does usually involve two or more people. Most people slamming were HIV+, variable ages (25-59), practicing hard sex (e.g. fisting) and were employed. Physical and psychological complications (including addiction) were observed in some of them. Some strategies, like not learning to inject themselves were implemented in order to better control the drug use. They declared not attending specialized services because they do not identify as typical injecting drug users. As a matter of fact, this was one of the main barriers to have a more ease access to materials and preventive information, not only because the refuse to ask for help in specialized services but also because HIV practitioners do not perceive their gay patients as potential drug users. Interviewees asked for physicians to be at least informed about slam and drug consumption in general, in order to ensure a more effective linkage to care if needed (including just thinking of asking about drug consumption). Finally, even the large already existing evidence about the relationship among sex and drug use, a lack of preventive actions and discourses considering both behaviors became manifest.
Although qualitative research regarding injecting drug use among gay men is scarce, it is a rich source of information about this phenomenon and can help develop adequate and appropriate political, institutional and associative responses.

With the rapid advancement of laser scanning and photogrammetry technologies, geometric data collection at construction sites by contractors has been increased to improve constructability, productivity, and onsite safety. Especially, the latest laser scanning technology provides faster scanning-speed, longer ranges, and higher accuracy and resolutions. However, the conventional static laser scanning method suffers from operational limitations due to the presence of many occlusions commonly found in a typical construction site. Full scanning without information loss requires that the scanning location should be changed several times, which also leads to extra work for registering each scanned point cloud. Alternatively, this paper presents an autonomous mobile robot which navigates a construction site and continuously updates a progress of 3D scanning with point clouds. This mobile robot system uses the 3D simultaneous localization and mapping (SLAM) technique to determine its navigation paths in an unknown environment, capture the survey-quality RGB mapped point cloud data, and automatically register the scans for geometric reconstruction of a construction site. The performance of the overall system was tested in indoor environments and validated with promising results.

This paper discusses enabling autonomous mobile robots to operate in unstructured terrain and environments and gather data in a time-efficient manner. In rugged environments such as construction sites and disaster sites, spatiotemporal data is difficult to acquire since these environments are potentially hazardous to humans. Autonomous robot systems represent a reasonable solution to provide topographic and surveillance data that can assist human activity in these environments, whether for exploration, mapping, or search and rescue. However, automatically operating the mobile laser scanning robots at cluttered environments is challenging because as-is geometrical conditions of the site are difficult to comprehend from the ground level due to the blocked line-of-site views. To address the issue, this paper introduces a new framework for operating mobile robots equipped with a laser scanning system in cluttered outdoor environments with the aid of an unmanned aerial vehicle (UAV). To obtain an initial map from the current field, this method first deploys UAV to collect photographic images of a site and builds a point cloud of a3D terrain of the site including obstacle information. A voxel grid is then created from the UAV-generated point cloud, and simulation for laser scan planning is conducted to determine the stationary laser scan positions at which a mobile robot can collect data with less occluded views while capturing crucial geometric information as much as it can. Finally, optimal paths for the mobile robot to navigate among the estimated scan positions are generated. Promising test results were obtained from a real-world outdoor structural material test yard as an example of a cluttered environment. It is expected that the proposed UAV-assisted robotic approach can significantly reduce human intervention and time for data collection and processing and provide technologies to enable cluttered environments to be frequently monitored, updated, and analyzed to support timely decision-making.

For autonomous navigation in restricted environments, micro
aerial vehicles (MAV) need to create 3D maps of their surroundings
and must track their motion within these maps. In this paper, we propose
an approach to simultaneous localization and mapping that is based on
the measurements of a lightweight 3D laser-range nder. We aggregate
laser-range measurements by registering sparse 3D scans with a local
multiresolution surfel map that has high resolution in the vicinity of
the MAV and coarser resolutions with increasing distance, which corresponds
well to measurement density and accuracy of our sensor. Modeling
measurement distributions within voxels by surface elements allows for
ecient and accurate registration of 3D scans with the local map. The
incrementally built local dense 3D maps of nearby key poses are registered
globally by graph optimization. This yields a globally consistent
dense 3D map of the environment. Continuous registration of local maps
with the global map allows for tracking the 6D MAV pose in real time.
In experiments, we demonstrate accuracy and eciency of our approach.

The digital acquisition of Cultural Heritage is a complex process, highly depending on the nature of the object as well as the purpose of its detection. Even if there are different survey techniques and sensors that allow the generation of realistic 3D models, defined by a good metric quality and a detail consistent with the geometric characteristics of the object, an interesting goal could be to develop a unified treatment of the methodologies. The Chapel of the Holy Shroud, with its intricate articulation, becomes the benchmark to test an integrated protocol between a terrestrial laser scanner (TLS) and a wearable mobile laser system (WMLS) based on a SLAM approach. In order to quantify the accuracy and precision of the latter solution, several forms of comparison are proposed. For the case study the ZEB-REVO, produced and marketed by GeoSLAM, is tested. Computations of cloud-to-cloud (C2C) absolute distances, comparisons of slices and extractions of planar features are performed, using stationary laser scanner (Faro Focus S S350) as a reference. Finally, the obtained results are reported, allowing us to assert that the quality of the WMLS measurements is compatible with the data provided by the manufacturer, thus making the instrumentation suitable for certain specific applications.

In this paper it is presented an integration of algorithms that permits maps construction and navigation of mobile robots. Simultaneous Localization and Mapping (SLAM) algorithm is used based on FastSLAM method. Navigation system is based on VHF to avoid obstacle and a spiral way trajectory method. To three different complex simulation maps are used to evaluate the system.

This paper proposes a Hybrid Approximate Representation (HAR) based on unifying several efficient approximations of the
generalized reprojection error (which is known as the gold standard for multiview geometry). The HAR is an over-parameterization
scheme where the approximation is applied simultaneously in multiple parameter spaces. A joint minimization scheme “HAR-Descent”
can then solve the PnP problem efficiently, while remaining robust to approximation errors and local minima.
The technique is evaluated extensively, including numerous synthetic benchmark protocols and the real-world data evaluations used in
previous works. The proposed technique was found to have runtime complexity comparable to the fastest O(n) techniques, and up to
10 times faster than current state of the art minimization approaches. In addition, the accuracy exceeds that of all 9 previous
techniques tested, providing definitive state of the art performance on the benchmarks, across all 90 of the experiments in the paper
and supplementary material.

This paper presents a complete Simultaneous Localization and Mapping (SLAM) solution for indoor mobile robots, addressing feature extraction, autonomous exploration and navigation using the continuously updating map. The platform used is Pioneer PeopleBot equipped with SICK Laser Measurment System (LMS) and odometery. Our algorithm uses Hough Transform to extract the major representative features of indoor environment such as lines and edges. Localization is accomplished using Relative Filter which depends directly on the perception model for the correction of error in the robot state. Our map for localization is in the form of a landmark network whereas for navigation we are using occupancy grid. The resulting algorithm makes the approach computationally lightweight and easy to implement. Finally, we present the results of testing the algorithm in Player/Stage as well as on PeopleBot in our Robotics and Control Lab.

Object discovery and modeling have been widely
studied in the computer vision and robotics communities.
SLAM approaches that make use of objects and higher level
features have also recently been proposed. Using higher level
features provides several benefits: these can be more discriminative,
which helps data association, and can serve to inform
service robotic tasks that require higher level information,
such as object models and poses. We propose an approach
for online object discovery and object modeling, and extend a
SLAM system to utilize these discovered and modeled objects as
landmarks to help localize the robot in an online manner. Such
landmarks are particularly useful for detecting loop closures
in larger maps. In addition to the map, our system outputs
a database of detected object models for use in future SLAM
or service robotic tasks. Experimental results are presented to
demonstrate the approach’s ability to detect and model objects,
as well as to improve SLAM results by detecting loop closures.

Simultaneous localization and mapping (SLAM) represents a fundamental problem for autonomous embodied systems, for which the hippocampal/entorhinal system (H/E-S) has been optimized over the course of evolution. We have developed a biologically-inspired SLAM architecture based on latent variable generative modeling within the Free Energy Principle and Active Inference (FEP-AI) framework, which affords flexible navigation and planning in mobile robots. We have primarily focused on attempting to reverse engineer H/E-S ‘design’ properties, but here we consider ways in which SLAM principles from robotics may help us better understand nervous systems and emergent minds. After reviewing LatentSLAM and notable features of this control architecture, we consider how the H/E-S may realize these functional properties not only for physical navigation, but also with respect to high-level cognition understood as generalized simultaneous localization and mapping (G-SLAM). We focus on loop-closure, graph-relaxation, and node duplication as particularly impactful architectural features, suggesting these computational phenomena may contribute to understanding cognitive insight (as proto-causal-inference), accommodation (as integration into existing schemas), and assimilation (as category formation). All these operations can similarly be describable in terms of structure/category learning on multiple levels of abstraction. However, here we adopt an ecological rationality perspective, framing H/E-S functions as orchestrating SLAM processes within both concrete and abstract hypothesis spaces. In this navigation/search process, adaptive cognitive equilibration between assimilation and accommodation involves balancing tradeoffs between exploration and exploitation; this dynamic equilibrium may be near optimally realized in FEP-AI, wherein control systems governed by expected free energy objective functions naturally balance model simplicity and accuracy. With respect to structure learning, such a balance would involve constructing models and categories that are neither too inclusive nor exclusive. We propose these (generalized) SLAM phenomena may represent some of the most impactful sources of variation in cognition both within and between individuals, suggesting that modulators of H/E-S functioning may potentially illuminate their adaptive significances as fundamental cybernetic control parameters. Finally, we discuss how understanding H/E-S contributions to G-SLAM may provide a unifying framework for high-level cognition and its potential realization in artificial intelligences.