Institute of Fundamental Technological Research

This paper presents open-source software for large-scale 3D mapping using an open-hardware hand-held measurement device. This work is dedicated to educational and research purposes. This software is composed of three components: LIDAR odometry, single-session refinement and multi-session refinement. Data refinement uses a pose-graph loop closure technique and an Iterative Closest Point algorithm to minimize the error of the edge. The results are 3D point clouds in LAZ data format (compressed LAS - LIDAR Aerial Survey). It was tested in many real-world scenarios/applications: city-level 3D mapping, culture heritage, creating ground truth data for mobile robots, precise forestry, and large-scale indoor 3D mapping. This software can run on Linux and Windows machines, it does not incorporate GPU computing. It is advised to use at least 32 GB of RAM to cope with large data sets. The optimization framework is implemented from scratch using the Eigen library, thus there is not need to install any additional libraries such as Ceres, g2o, GTSAM, manif, Sophus etc.

A mobile mapping, Lidar odometry, Loop closure, Iterative closest point, Data registration, SLAM

This paper describes a novel approach for nuclear facility inspection with novel automated 3D mapping system as an open source end to end navigation stack available at https://github.com/JanuszBedkowski/msas_enrich_2023. Incidents such as Fukushima, Majak or Chernobyl as well as the decommissioning and dismantling of old nuclear facilities (e.g. Sellafield, Asse or Murmansk) are showing great importance of the robotic technology. Rapid inspection requires reliable, accurate, precise and repeatable simultaneous localization and mapping. Proposed SLAM approach uses only non repetitive scanning pattern Lidar (Livox Mid360) and integrated inertial measurement unit. The novelty is based on feature less single core SLAM implementation. It fuses Normal Distributions Transform and motion model for simultaneous map building and current pose estimation. Motion model bounds an optimization result, thus it is stable and reliable. It requires less than 10 ms for pose update, trajectory tracking and emergency behavior. This method is a candidate for real time application since a calculation time is bounded and it uses only one core of Intel Celeron CPU G1840 2.8 GHz. It was tested both (i) during EnRicH 2023 https://enrich.european-robotics.eu/ — the European robotics hackathon, (ii) in laboratory conditions. This open source project provides also software of base station, thus it is first end to end solution available in literature.

Navigation, Inspection, Mobile robot, SLAM, Localization

The motivation behind this research is the lack of an underground mining shaft data set in the literature in the form of open access. For this reason, our data set can be used for many research purposes such as shaft inspection, 3D measurements, simultaneous localization and mapping, artificial intelligence, etc. The data collection method incorporates rotated Velodyne VLP-16, Velodyne Ultra Puck VLP-32c, Livox Tele-15, IMU Xsens MTi-30 and Faro Focus 3D. The ground truth data were acquired with a geodetic survey including 15 ground control points and 6 Faro Focus 3D terrestrial laser scanner stations of a total 273,784,932 of 3D measurement points. This data set provides an end-user case study of realistic applications in mobile mapping technology. The goal of this research was to fill the gap in the underground mining data set domain. The result is the first open-access data set for an underground mining shaft (shaft depth −300 m).

LiDAR, IMU, underground shaft mapping, mine mapping

This study addresses multi-view Terrestrial Laser Scanning Point Cloud data registration methods. Multiple rigid point cloud data registration is mandatory for aligning all scans into a common reference frame and it is still considered a challenge looking from a large-scale surveys point of view. The goal of this work is to support the development of cutting-edge registration methods in geoscience and mobile robotics domains. This work evaluates 3 data sets of total 20 scenes available in the literature. This paper provides a novel opensource framework for multi-view Terrestrial Laser Scanning Point Cloud data registration benchmarks. The goal was to verify experimentally which registration variant can improve the open-source data looking from the quantitative and qualitative points of view. In particular, the following scanners provided measurement data: Z+F TLS Imager 5006i, Z+F TLS Imager 5010C, Leica ScanStation C5, Leica ScanStation C10, Leica P40 and Riegl VZ-400. The benchmark shows an impact of the metric e.g. point to point, point to projection onto a plane, plane to plane etc..., rotation matrix parameterization (Tait–Bryan, quaternion, Rodrigues) and other implementation variations (e.g. multi-view Normal Distributions Transform, Pose Graph SLAM approach) onto the multi-view data registration accuracy and performance. An open-source project is created and it can be used for improving existing data sets reported in the literature, it is the added value of the presented research. The combination of metrics, rotation matrix parameterization and optimization algorithms creates hundreds of possible approaches. It is shown that chosen metric is a dominant factor in data registration. The rotation parameterization and other degrees of freedom of proposed variants are rather negligible compared with chosen metric. Most of the proposed approaches improve registered reference data provided by other researchers. Only for 2 from 20 scenes it was not possible to provide significant improvement. The largest improvements are evident for large-scale scenes. The project is available and maintained at https://github.com/MapsHD/HDMapping.

TLS, Point cloud, Open-source, Multi-view data registration, LiDAR data metrics, Robust loss function, Tait–Bryan angles, Quaternions, Rodrigues’ formula, Lie algebra, Rotation matrix parameterization

This paper describes an affordable robotic mobile 3D mapping system. It is built with Livox Mid−40 lidar with a conic field of view extended by a custom rotating planar reflector. This 3D sensor is compared with the more expensive Velodyne VLP 16 lidar. It is shown that the proposed sensor reaches satisfactory accuracy and range. Furthermore, it is able to preserve the metric accuracy and non−repetitive scanning pattern of the unmodified sensor. Due to preserving the non−repetitive scan pattern, our system is capable of covering the entire field of view of 38.4 × 360 degrees, which is an added value of conducted research. We show the calibration method, mechanical design, and synchronization details that are necessary to replicate our system. This work extends the applicability of solid−state lidars since the field of view can be reshaped with minimal loss of measurement properties. The solution was part of a system that was evaluated during the 3rd European Robotics Hackathon in the Zwentendorf Nuclear Power Plant. The experimental part of the paper demonstrates that our affordable robotic mobile 3D mapping system is capable of providing 3D maps of a nuclear facility that are comparable to the more expensive solution.

automatic calibration, solid-state lidar, reshape field of view, 3D mapping, SLAM, robotic mapping

This paper describes the calibration method for calculating parameters (position and orientation) of planar reflectors reshaping LiDAR’s (light detection and ranging) field of view. The calibration method is based on the reflection equation used in the ICP (Iterative Closest Point) optimization. A novel calibration process as the multi-view data registration scheme is proposed; therefore, the poses of the measurement instrument and parameters of planar reflectors are calculated simultaneously. The final metric measurement is more accurate compared with parameters retrieved from the mechanical design. Therefore, it is evident that the calibration process is required for affordable solutions where the mechanical design can differ from the inaccurate assembly. It is shown that the accuracy is less than 20 cm for almost all measurements preserving long-range capabilities. The experiment is performed based on Livox Mid-40 LiDAR augmented with six planar reflectors. The ground-truth data were collected using Z + F IMAGER 5010 3D Terrestrial Laser Scanner. The calibration method is independent of mechanical design and does not require any fiducial markers on the mirrors. This work fulfils the gap between rotating and Solid-State LiDARs since the field of view can be reshaped by planar reflectors, and the proposed method can preserve the metric accuracy. Thus, such discussion concludes the findings. We prepared an open-source project and provided all the necessary data for reproducing the experiments. That includes: Complete open-source code, the mechanical design of reflector assembly and the dataset which was used in this paper.

LiDAR, ICP, mapping, calibration, reshape field of view, solid state LiDAR

Mine shafts are governed by special rules, often difficult to access and known for their unfavourable conditions for measurements. A mine shaft is of strategic importance for the proper functioning of the mine in terms of air ventilation, transport of people and material extraction, for example. Therefore, not least for safety reasons, mine shafts require regular inspections and surveys. For this purpose, the company SKALA 3D has developed an automated mobile laser scanning system as a mining survey system (MSS) which facilitates fast and precise measurements. The result is a comprehensive 3D model of the mine shaft ready for further analysis.

*Purpose* This paper aims to focus on real-world mobile systems, and thus propose relevant contribution to the special issue on „Real-world mobile robot systems”. This work on 3D laser semantic mobile mapping and particle filter localization dedicated for robot patrolling urban sites is elaborated with a focus on parallel computing application for semantic mapping and particle filter localization. The real robotic application of patrolling urban sites is the goal; thus, it has been shown that crucial robotic components have reach high Technology Readiness Level (TRL). *Design/methodology/approach* Three different robotic platforms equipped with different 3D laser measurement system were compared. Each system provides different data according to the measured distance, density of points and noise; thus, the influence of data into final semantic maps has been compared. The realistic problem is to use these semantic maps for robot localization; thus, the influence of different maps into particle filter localization has been elaborated. A new approach has been proposed for particle filter localization based on 3D semantic information, and thus, the behavior of particle filter in different realistic conditions has been elaborated. The process of using proposed robotic components for patrolling urban site, such as the robot checking geometrical changes of the environment, has been detailed. *Findings* The focus on real-world mobile systems requires different points of view for scientific work. This study is focused on robust and reliable solutions that could be integrated with real applications. Thus, new parallel computing approach for semantic mapping and particle filter localization has been proposed. Based on the literature, semantic 3D particle filter localization has not yet been elaborated; thus, innovative solutions for solving this issue have been proposed. Recently, a semantic mapping framework that was already published was developed. For this reason, this study claimed that the authors' applied studies during real-world trials with such mapping system are added value relevant for this special issue. *Research limitations/implications* The main problem is the compromise between computer power and energy consumed by heavy calculations, thus our main focus is to use modern GPGPU, NVIDIA PASCAL parallel processor architecture. Recent advances in GPGPUs shows great potency for mobile robotic applications, thus this study is focused on increasing mapping and localization capabilities by improving the algorithms. Current limitation is related with the number of particles processed by a single processor, and thus achieved performance of 500 particles in real-time is the current limitation. The implication is that multi-GPU architectures for increasing the number of processed particle can be used. Thus, further studies are required. *Practical implications* The research focus is related to real-world mobile systems; thus, practical aspects of the work are crucial. The main practical application is semantic mapping that could be used for many robotic applications. The authors claim that their particle filter localization is ready to integrate with real robotic platforms using modern 3D laser measurement system. For this reason, the authors claim that their system can improve existing autonomous robotic platforms. The proposed components can be used for detection of geometrical changes in the scene; thus, many practical functionalities can be applied such as: detection of cars, detection of opened/closed gate, etc. [...] These functionalities are crucial elements of the safe and security domain. *Social implications* Improvement of safe and security domain is a crucial aspect of modern society. Protecting critical infrastructure plays an important role, thus introducing autonomous mobile platforms capable of supporting human operators of safe and security systems could have a positive impact if viewed from many points of view. *Originality/value* This study elaborates the novel approach of particle filter localization based on 3D data and semantic mapping. This original work could have a great impact on the mobile robotics domain, and thus, this study claims that many algorithmic and implementation issues were solved assuming real-task experiments. The originality of this work is influenced by the use of modern advanced robotic systems being a relevant set of technologies for proper evaluation of the proposed approach. Such a combination of experimental hardware and original algorithms and implementation is definitely an added value.

3D laser, Monte Carlo localization, Parallel computing, Particle filter localization, Semantic mapping, Unmanned ground vehicle

This work concerns the study of 6DSLAM algorithms with an application of robotic mobile mapping systems. The architecture of the 6DSLAM algorithm is designed for evaluation of different data registration strategies. The algorithm is composed of the iterative registration component, thus ICP (Iterative Closest Point), ICP (point to projection), ICP with semantic discrimination of points, LS3D (Least Square Surface Matching), NDT (Normal Distribution Transform) can be chosen. Loop closing is based on LUM and LS3D. The main research goal was to investigate the semantic discrimination of measured points that improve the accuracy of final map especially in demanding scenarios such as multi-level maps (e.g., climbing stairs). The parallel programming based nearest neighborhood search implementation such as point to point, point to projection, semantic discrimination of points is used. The 6DSLAM framework is based on modified 3DTK and PCL open source libraries and parallel programming techniques using NVIDIA CUDA. The paper shows experiments that are demonstrating advantages of proposed approach in relation to practical applications. The major added value of presented research is the qualitative and quantitative evaluation based on realistic scenarios including ground truth data obtained by geodetic survey. The research novelty looking from mobile robotics is the evaluation of LS3D algorithm well known in geodesy.

Mobile robot, Mobile mapping system, Iterative Closest Point, Least Square Surface Matching, Normal Distribution Transform, LUM, 6DSLAM, CUDA

Search-and-rescue operations have recently been confronted with the introduction of robotic tools that assist the human search-and-rescue workers in their dangerous but life-saving job of searching for human survivors after major catastrophes. However, the world of search and rescue is highly reliant on strict procedures for the transfer of messages, alarms, data, and command and control over the deployed assets. The introduction of robotic tools into this world causes an important structural change in this procedural toolchain. Moreover, the introduction of search-and-rescue robots acting as data gatherers could potentially lead to an information overload toward the human search-and-rescue workers, if the data acquired by these robotic tools are not managed in an intelligent way. With that in mind, we present in this paper an integrated data combination and data management architecture that is able to accommodate real-time data gathered by a fleet of robotic vehicles on a crisis site, and we present and publish these data in a way that is easy to understand by end-users. In the scope of this paper, a fleet of unmanned ground and aerial search-and-rescue vehicles is considered, developed within the scope of the European ICARUS project. As a first step toward the integrated data-management methodology, the different robotic systems require an interoperable framework in order to pass data from one to another and toward the unified command and control station. As a second step, a data fusion methodology will be presented, combining the data acquired by the different heterogenic robotic systems. The computation needed for this process is done in a novel mobile data center and then (as a third step) published in a software as a service (SaaS) model. The SaaS model helps in providing access to robotic data over ubiquitous Ethernet connections. As a final step, we show how the presented data-management architecture allows for reusing recorded exercises with real robots and rescue teams for training purposes and teaching search-and-rescue personnel how to handle the different robotic tools. The system was validated in two experiments. First, in the controlled environment of a military testing base, a fleet of unmanned ground and aerial vehicles was deployed in an earthquake-response scenario. The data gathered by the different interoperable robotic systems were combined by a novel mobile data center and presented to the end-user public. Second, an unmanned aerial system was deployed on an actual mission with an international relief team to help with the relief operations after major flooding in Bosnia in the spring of 2014. Due to the nature of the event (floods), no ground vehicles were deployed here, but all data acquired by the aerial system (mainly three-dimensional maps) were stored in the ICARUS data center, where they were securely published for authorized personnel all over the world. This mission (which is, to our knowledge, the first recorded deployment of an unmanned aerial system by an official governmental international search-and-rescue team in another country) proved also the concept of the procedural integration of the ICARUS data management system into the existing procedural toolchain of the search and rescue workers, and this in an international context (deployment from Belgium to Bosnia). The feedback received from the search-and-rescue personnel on both validation exercises was highly positive, proving that the ICARUS data management system can efficiently increase the situational awareness of the search-and-rescue personnel.

This paper concerns the an intelligent mobile application for spatial design support and security domain. Mobility has two aspects in our research: The first one is the usage of mobile robots for 3D mapping of urban areas and for performing some specific tasks. The second mobility aspect is related with a novel Software as a Service system that allows access to robotic functionalities and data over the Ethernet, thus we demonstrate the use of the novel NVIDIA GRID technology allowing to virtualize the graphic processing unit. We introduce Complex Shape Histogram, a core component of our artificial intelligence engine, used for classifying 3D point clouds with a Support Vector Machine. We use Complex Shape Histograms also for loop closing detection in the simultaneous localization and mapping algorithm. Our intelligent mobile system is built on top of the Qualitative Spatio-Temporal Representation and Reasoning framework. This framework defines an ontology and a semantic model, which are used for building the intelligent mobile user interfaces. We show experiments demonstrating advantages of our approach. In addition, we test our prototypes in the field after the end-user case studies demonstrating a relevant contribution for future intelligent mobile systems that merge mobile robots with novel data centers.

Intelligent mobile system, 3D object recognition, Qualitative representation and reasoning, 3D mapping

In this paper a quantitative and qualitative evaluation of proposed ICP-based data registration algorithm, improved by parallel programming in CUDA (compute unified device architecture), is shown. The algorithm was tested on data collected with a 3D terrestrial laser scanner Z + F Imager 5010 mounted on the mobile platform PIONNER 3AT. Parallel implementation enables data registration on-line, even using a laptop with a standard hardware configuration (graphic card NVIDIA GeForce 6XX/7XX series). Robustness is assured by the use of CUDA-enhanced fast NNS (nearest neighbor search) applied for ICP (iterative closest point) with SVD (singular value decomposition) solver. The evaluation is based on the reference ground truth data registered with geodetic precision. The geodetic approach extends our previous work and gives an accurate benchmark for the algorithm. The data were collected in an urban area under a demolition scenario in a real environment. We compared four registration strategies concerning data preprocessing, such as subsampling and vegetation removal. The result is the analysis of measured performance and the accuracy of the geometric maps. The system provides accurate metric maps on-line and can be used in several applications such as mobile robotics for construction area modelling or spatial design support. It is a core component for our future work on mobile mapping systems.

Iterative closest point, Data registration, Mobile mapping, CUDA parallel programming, Spatial design support

This paper describes the methodology behind intelligent mobile assistant for spatial design support. The assistant gathers data and provides computational support for spatial assistance system on the basis of making intelligent spatial decisions. The main idea behind the assistant is to create a semantic model of the environment and performing preliminary spatial reasoning to provide cognitive feedback. The main goal is to support the designer in his task by perceiving and evaluating spatial design intent. Simultaneously the assistant allows for on-line modeling of real structured environment. It improves the conception–modeling–evaluation–remodeling cycle. This paper also contains an empirical evaluation of the proposed methodology. The results of the experiments performed using the prototype of Mobile Spatial Assistance System (MSAS) are shown. To conclude, the article presents the new methodology behind spatial support systems, which provides designers with cognitive assistance.

Semantic mapping, Spatial reasoning, Mobile embodiment for spatial design support system, On-line cognitive feedback

This paper concerns a classification problem of 3 D shapes being in motion. The goal is to develop the system with real-time capabilities to distinguish basic shapes (corners, planes, cones, spheres etc.) that are moving in front of RGB-D sensor. It is introduced an improvement of SoA algorithms (normal vector computation using PCA Principal Component Analysis and SVD Singular Value Decomposition, PFH – Point Feature Histogram) based on GPGPU (General Purpose Graphic Processor Unit) computation. This approach guarantee on-line computation of normal vectors, unfortunately computation time of the PFH for each normal vector is still a challenge to obtain on-line capabilities, therefore in this paper it is shown how to find a region of movement and to perform the classification process assuming the decreased amount of data. Proposed approach can be a starting point for further developments of the systems able to recognize the objects in the dynamic environments.

RGB-D camera, point cloud, normal vector estimation, point feature histogram, parallel programming

W artykule przedstawiono projekt autonomicznego robota inspekcyjnego. Ze względu na fakt, że komercyjne rozwiązania nie oferują satysfakcjonującej funkcjonalności w stosunkowo niskiej cenie zdecydowano się zaprojektować autonomicznego robota inspekcyjnego na bazie komercyjnej platformy wyposażonej w autorskie rozwiązanie laserowego systemu pomiarowego 3D. Projekt lasera 3D wykonano z wykorzystaniem technik szybkiego prototypowania metodą druku 3D. Autonomiczny robot mobilny nawigowany jest na podstawie systemu IMU (Inertial Measurement Unit) ze zintegrowanym GPS (Global Positioning System). Opracowane rozwiązanie dostarcza użytkownikowi danych w postaci map lokalnych 3D wraz z częściową analizą semantyczną (obliczanie wektorów normalnych dla chmury punktów metodą PCA Principal Component Analysis) w trybie on-line. Przeprowadzono eksperymenty weryfikujące poprawność działania systemu. W rezultacie powstało nowoczesne stanowisko badawcze, które może być wykorzystane do kolejnych badań z wykorzystaniem mobilnych systemów inspekcyjnych.

robot inspekcyjny, laserowy system pomiarowy 3D, PCA (Principal Component Analysis)

This paper discusses the methodology of Qualitative Spatio-Temporal Representation and Reasoning (QSTRR) for robotic applications. The goal is to develop reasoning mechanism that will allow modelling the environment and performing spatiotemporal decisions. A new approach is related to environment modelling based on robot’s perception, therefore new concepts (spatial entities) are obtained automatically, and then used in reasoning. This paper presents the results of the three experiments. Each experiment focuses on different robotic applications, such as mobile spatial assistive intelligence for spatial design, spatial design used for robotic arm integration with the environment and supervision of a teleoperated robot. Each of the experiments is considered as the proof of concept of the proposed methodology. Thus, it can be efficiently used for developing sophisticated robotic application where human-robot interaction and integration are considered as an important goal.

qualitative reasoning, mobile robot, industrial robot, semantic modelling

This paper deals with research related with the improvements of state of the art algorithms used in robotic applications based on parallel computation. The main goal is to decrease the computational complexity of 3D cloud of points processing in applications as: data filtering, normal vector estimation, data registration, and point feature histogram calculation. The presented results improve the efficiency of existing implementations with minimal lost of accuracy. The main contribution is a regular grid decomposition originally implemented for nearest neighborhood search. This data structure is the basis for all presented methods, it provides an efficient method for decreasing the time of computation. The results are compared with well-known robotic frameworks such as PCL and 3DTK.

This paper concerns implementation of algorithms in the two important aspects of modern 3D data processing: data registration and segmentation. Solution proposed for the first topic is based on the 3D space decomposition, while the latter on image processing and local neighbourhood search. Data processing is implemented by using NVIDIA compute unified device architecture (NIVIDIA CUDA) parallel computation. The result of the segmentation is a coloured map where different colours correspond to different objects, such as walls, floor and stairs. The research is related to the problem of collecting 3D data with a RGB−D camera mounted on a rotated head, to be used in mobile robot applications. Performance of the data registration algorithm is aimed for on−line processing. The iterative closest point (ICP) approach is chosen as a registration method. Computations are based on the parallel fast nearest neighbour search. This procedure decomposes 3D space into cubic buckets and, therefore, the time of the matching is deterministic. First technique of the data segmentation uses accelrometers integrated with a RGB−D sensor to obtain rotation compensation and image processing method for defining prerequisites of the known categories. The second technique uses the adapted nearest neighbour search procedure for obtaining normal vectors for each range point.

3D data registration, image segmentation, GPGPU

Purpose – The purpose of this paper is to demonstrate a real time 3D localization and mapping approach for the USAR (Urban Search and Rescue) robotic application, focusing on the performance and the accuracy of the General-purpose computing on graphics processing units (GPGPU)-based iterative closest point (ICP) 3D data registration implemented using modern GPGPU with FERMI architecture. Design/methodology/approach – The authors put all the ICP computation into GPU, and performed the experiments with registration up to 106 data points. The main goal of the research was to provide a method for real-time data registration performed by a mobile robot equipped with commercially available laser measurement system 3D. The main contribution of the paper is a new GPGPU based ICP implementation with regular grid decomposition. It guarantees high accuracy as equivalent CPU based ICP implementation with better performance. Findings – The authors have shown an empirical analysis of the tuning of GPUICP parameters for obtaining much better performance (acceptable level of the variance of the computing time) with minimal lost of accuracy. Loop closing method is added and demonstrates satisfactory results of 3D localization and mapping in urban environments. This work can help in building the USAR mobile robotic applications that process 3D cloud of points in real time. Practical implications – This work can help in developing real time mapping for USAR robotic applications. Originality/value – The paper proposes a new method for nearest neighbor search that guarantees better performance with minimal loss of accuracy. The variance of computational time is much less than SoA.

Robotics, Search and rescue, Mapping, Data handling, Data registration, Point to point, Iterative closest point, General-purpose computing on graphics processing units

The paper concerns the research related to the improvement of control and supervision of web connected mobile robots using Physic Processing Unit (PPU). PPU computations taken into the consideration include rigid body dynamics, collision detection and raycasting. The result is improved by Human Machine Interface that allows performing semantic simulation during multi robot task execution. Semantic simulation engine provides tools to implement the mobile robot simulation, which is based on real data delivered by robot’s observations in INDOOR environment. The supervision of real objects such as robots is performed by association with its virtual representation in the simulation, therefore events such as object intersection, robot orientation - pitch and roll are able to be monitored. The simulation can be integrated with real part of the system with an assumption of robust localization of real entities, therefore Augmented Reality capabilities are available.

semantic mapping, Human Machine Interface, mobile robot

W artykule przedstawiono mobilny system tworzenia przestrzennej dokumentacji semantycznej. Zaproponowano nową metodę filtracji oraz rejestracji danych wykorzystującą obliczenia równoległe (NVIDIA FERMI). Opracowany system informatyczny umożliwia gromadzenie danych przestrzennych z wykorzystaniem geodezyjnego systemu pomiarowego 3D oraz pozwala na etykietowanie obiektów. Tworzona mapa semantyczna jest dostępna z poziomu dowolnego urządzenia mobilnego (laptop, smartphone, tablet).

mapa semantyczna, przestrzenna dokumentacja semantyczna, skanowanie laserowe, chmura punktów, obliczenia równoległe, wizualizacja

The main goal was to improve a state of the art 6D SLAM algorithm with a new GPGPU-based implementation of data registration module. Data registration is based on ICP (Iterative Closest Point) algorithm that is fully implemented in the GPU with NVIDIA FERMI architecture. In our research we focus on mobile robot inspection intervention systems applicable in hazardous environments. The goal is to deliver a complete system capable of being used in real life. In this paper we demonstrate our achievements in the field of on line robot localization and mapping. We demonstrated an experiment in real large environment. We compared two strategies of data alingment - simple ICP and ICP using so called meta scan.

6D SLAM, parallel computation

The paper concerns the results related with GPGPU computing applied for mobile robotics applications. The scalable implementation of the point to point and point to plane 3D data registration methods with an improvement based on regular grid decomposition is shown. 3D data is delivered by mobile robot equipped with 3D laser measurement system for IND OOR environments. Presented empirical analysis of the implementation shows the On-Line computation capability using modern graphic processor unit NVIDIA GF 580. In the paper the discussion concerning the comparison between these two methods is given. It will be shown why the point to plain ICP implementation can achieve better performance than the point to point approach. We show parallel vector computation that is used for semantic objects identifications and for loop closing detection.

Data registration, parallel computing, point to point, point to plane, mobile robot

In this paper the GPU-accelerated range sensors simulation is discussed. Range sensors generate large amount of data per second and to simulate these high-performance simulation is needed. We propose to use parallel ray tracing on graphics processing units to improve the performance of range sensors simulation. The multiple range sensors are described and simulated using NVIDIA OptiX ray tracing engine. This work is focused on the performance of the GPU acceleration of range images simulation in complex environments. Proposed method is tested using several state-of-the-art ray tracing datasets. The software is publicly available as an open-source project SensorSimRT.

Ray tracing, RGB-D sensors, Simulation

We propose an open source robotic 3D mapping framework based on Robot Operating System, Point Cloud Library and Cloud Compare software extended by functionality of importing and exporting datasets. The added value is an integrated solution for robotic 3D mapping and new publicly available datasets (accurate 3D maps with geodetic precision) for evaluation purpose Datasets were gathered by mobile robot in stop scan fashion. Presented results are a variety of tools for working with such datasets, for task such as: preprocessing (filtering, down sampling), data registration (ICP, NDT), graph optimization (ELCH, LUM), tools for validation (comparison of 3D maps and trajectories), performance evaluation (plots of various outputs of algorithms). The tools form a complete pipeline for 3D data processing. We use this framework as a reference methodology in recent work on SLAM algorithms.

Three-dimensional displays, Robot kinematics, Cameras, Mobile communication, Robot sensing systems, XML

This paper concentrates on the topic of gathering, processing and presenting 3D data for use in Search and Rescue operations. The data are gathered by unmanned ground platforms, in form of 3D point clouds. The clouds are matched and transformed into a consistent, highly accurate 3D model. The paper describes the pipeline for such matching based on Iterative Closest Point algorithm supported by loop closing done with LUM method. The pipeline was implemented for parallel computation with Nvidia CUDA, which leads to higher matching accuracy and lower computation time. An analysis of performance for multiple GPUs is presented. The second problem discussed in the paper is immersive visualization of 3d data for search and rescue personnel. Five strategies are discussed: plain 3D point cloud, hypsometry, normal vectors, space descriptors and an approach based on light simulation through the use of NVIDIA OptiX Ray Tracing Engine. The results from each strategy were shown to end users for validation. The paper discusses the feedback given. The results of the research are used in the development of a support module for ICARUS project.

Three-dimensional displays, Data visualization, Graphics processing units, Image color analysis, Computational modeling, Solid modeling, Pipelines

This paper shows evaluation result of the mobile robotic system for Urban Search and Rescue performed during Eurathlon 2013 robotic competition by IAIR-IMM team. Our team was competing in two scenarios: a) Reconnaissance and surveillance in urban structures (USAR), b) Search and rescue in a smoke-filled underground structure. The main task for this system from our team point of view was to build 3D metric map of the environment and to find OPIs (Objects of Potential Interest). Therefore in this paper we described the vision system for objects recognition and 3D map building. The system is composed of mobile robot equipped with camera, 3D laser measurement system and base station composed of computer equipped with NVIDIA GPU for parallel processing of derived clouds of points. The main focus of the work was to improve the performance of the operator controlling the robot in harsh environment. We achieved satisfactory results that could be still improved in many aspects. In experimental part we demonstrated validation of vision recognition system and 3D maps built during preparation trials and during final competition. The best quantitative result of this work was 3rd place in USAR scenario. Unfortunately, we could not build the map in a smoke-filled underground structure, but the result is also very interesting for future developments.

Eurathlon, mobile robot

This article concerns current progress in the development of multi robot simulation for TIRAMISU project. This simulator is designed for training of UGV (Unmanned Ground Vehicles) operators in cooperative mission execution. The core components of the system are implemented using VORTEX physics simulation engine with OSG (Open Scene Graph) used for rendering. The engine provides an accurate physics simulation for robots working on a single stage. The main goal during development was to prepare a multi robot architecture for the simulation. The challenge was to integrate all simulation components into a common framework, therefore allowing the robots to interact with each other, without lose of simulation accuracy. Current version of the simulator has two types of robots: a) iRobot-PacBot b)LOCSTRA - a TIRAMISU robot for humanitarian demining. An example of multi robot scenario, transportation of UXO (UneXploded Ordnance), will be discussed.

Humanitarian demining, mobile robot simulation, operator training

Search and rescue (SAR) teams often face several complex and dangeroustasks, witch could be aided by unmanned robotic vehicles (UV). UV agents can potentially be used to decrease the risk in the loss of lives both of the rescuers and victims and aid in the search and transportation and survivors and in the removal of debris in a catastrophe scenario. Depending on the nature of a catastrophe and its geographical location, there are potentially three types of UV contemplates, their operators need prior training and certification. To train and certify the operators a tool (serious game) is under development. In this paper we will make an overview about our approach in its development. This game uses a typical client-server architecture where all client agents (virtual UVs and operator client interfaces) share the same immersive virtual environment which is generated through the merging of GIS data and a semantic model extracted from 3D lase data. There will be several types of scenarios suitable to several types of catastrophe situations.Each of these scenarios has its own mission plan for the trainees to follow. The game will also provide an interface for mission planning so that each mission plan will be carefully designed to accurately correspond to a matrix of skills. This matrix lists a set of common skills in various different UV operational case studies which will allow the certification of operators.

Affordable technological solutions are always welcome, thus we decided to test the backpack based 3D mapping system for digitizing USAR scenes. The system is composed of Intel RealSense Tracking Camera T265, three Velodynes VLP16, custom electronics for multi-lidar synchronization and VR Zotac GO backpack computer equipped with GeForce GTX1070. This configuration allows the operator to collect and process 3D point clouds to obtain a consistent 3D map. To reach satisfactory accuracy we use RealSense as initial guess of trajectory from Visual Odometry (VO). Lidar odometry corrects trajectory and reduces scale error from VO. The academic 6DSLAM is used for loop closure and finally classical ICP algorithm refines the final 3D point cloud. All steps can be done in the field in reasonable time. The VR backpack can be used for virtual travel over digital content afterwords. Additionally deep neural network is used to perform online object detection using Relsense camera input.