Autonomous Mapping Research Articles

Automated Geolocation in Urban Environments Using a Simple Camera-Equipped Unmanned Aerial Vehicle: A Rapid Mapping Surveying Alternative?

GNSS positioning accuracy can be degraded in areas where the surrounding object geometry and morphology interacts with the GNSS signals. Specifically, urban environments pose challenges to precise GNSS positioning because of signal interference or interruptions. Also, non-GNSS surveying methods, including total stations and laser scanners, involve time consuming practices in the field and costly equipment. The present study proposes the use of an Unmanned Aerial Vehicle (UAV) for autonomous rapid mapping that resolves the problem of localization for the drone itself by acquiring location information of characteristic points on the ground in a local coordinate system using simultaneous localization and mapping (SLAM) and vision algorithms. A common UAV equipped with a camera and at least a single known point, are enough to produce a local map of the scene and to estimate the relative coordinates of pre-defined ground points along with an additional arbitrary point cloud. The resulting point cloud is readily measurable for extracting and interpreting geometric information from the area of interest. Under two novel optimization procedures performing line and plane alignment of the UAV-camera-measured point geometries, a set of experiments determines that the localization of a visual point in distances reaching 15 m from the origin, delivered a level of accuracy under 50 cm. Thus, a simple UAV with an optical sensor and a visual marker, prove quite promising and cost-effective for rapid mapping and point localization in an unknown environment.

High-Throughput and Autonomous Grazing Incidence X-ray Diffraction Mapping of Organic Combinatorial Thin-Film Library Driven by Machine Learning.

High-throughput X-ray diffraction (XRD) is one of the most indispensable techniques to accelerate materials research. However, the conventional XRD analysis with a large beam spot size may not best appropriate in a case for characterizing organic materials thin film libraries, in which various films prepared under different process conditions are integrated on a single substrate. Here, we demonstrate that high-resolution grazing incident XRD mapping analysis is useful for this purpose: A 2-dimensional organic combinatorial thin film library with the composition and growth temperature varied along the two orthogonal axes was successfully analyzed by using synchrotron microbeam X-ray. Moreover, we show that the time-consuming mapping process is accelerated with the aid of a machine learning technique termed as Bayesian optimization based on Gaussian process regression.

Survey of Datafusion Techniques for Laser and Vision Based Sensor Integration for Autonomous Navigation.

This paper focuses on data fusion, which is fundamental to one of the most important modules in any autonomous system: perception. Over the past decade, there has been a surge in the usage of smart/autonomous mobility systems. Such systems can be used in various areas of life like safe mobility for the disabled, senior citizens, and so on and are dependent on accurate sensor information in order to function optimally. This information may be from a single sensor or a suite of sensors with the same or different modalities. We review various types of sensors, their data, and the need for fusion of the data with each other to output the best data for the task at hand, which in this case is autonomous navigation. In order to obtain such accurate data, we need to have optimal technology to read the sensor data, process the data, eliminate or at least reduce the noise and then use the data for the required tasks. We present a survey of the current data processing techniques that implement data fusion using different sensors like LiDAR that use light scan technology, stereo/depth cameras, Red Green Blue monocular (RGB) and Time-of-flight (TOF) cameras that use optical technology and review the efficiency of using fused data from multiple sensors rather than a single sensor in autonomous navigation tasks like mapping, obstacle detection, and avoidance or localization. This survey will provide sensor information to researchers who intend to accomplish the task of motion control of a robot and detail the use of LiDAR and cameras to accomplish robot navigation.

Autonomous underwater vehicles: Instrumentation and measurements

Oceans exploration and inspection are a great challenge for the industry nowadays. The underwater instrumentation and measurements are improving due to the current technologies, or by development of new ones, to cover the demand of the new industry offshore. The Autonomous Underwater Vehicle (AUV), a subcategory of submarine, is used to perform subaquatic tasks. This vehicle provides advantages for underwater works, e.g., safety and reliability inspections, but it also offers limitations for sensors systems, monitoring and communications systems, autonomous operational endurances, propulsion systems or mapping designs, etc. The main scientific contributions of this paper are: a review of the state of art in novel and main instrumentation and measurement systems embedded in AUVs; an illustration of their future uses and development; and a synthesis of the main and current navigation, mapping and sampling technologies, together with different applications.

Strategies for Autonomic Nervous System Mapping of Fiber Composition and Neural Circuitry Using Transmission Electron Microscopy in Support of SPARC Projects

The optimization of neuromodulation strategies is important for a development and refinement of therapies. The design of customized tools, such as electrical stimulating devices, depends on the detailed knowledge of nerve fiber composition and inter‐relationships between target organs, nerve populations, and synaptic circuitry. The autonomic nervous system gives support to organs and are composed by myelinated and unmyelinated fibers as well as pre‐ and post‐ganglionic neurons. Mapping an autonomic nerve such as the pelvic or vagus nerve that innervates peripheral tissues and understanding the synaptology of neurons in the cardiac and stellate ganglia may provide advanced knowledge for improved neuromodulation strategies. Transmission electron microscopy (TEM) is a high‐level technology that helps to identify and characterize axonal myelination and ultrastructure of unmyelinated fibers. TEM also allows for fine structural analysis of neural circuitry and innervation of autonomic targets. Immunogold detection techniques using TEM permits the identification and characterization of neuronal tracers in the nervous system. With the support from the Stimulating Peripheral Activity to Relieve Conditions (SPARC) program at NIH, we developed customized ultrastructural studies of the autonomic nervous system to facilitate refinement of neuromodulation strategies related to the autonomic control of cardiac, gastrointestinal, and lower urinary tract systems. Morphometric analysis is performed by automatic segmentation, and the development of a new strategy to calculate measurements of axon diameter, fiber diameter, myelin thickness, and G‐ratio provides a better understanding of fiber size for modelling studies. Both males and females from different species, including humans, are analyzed and compared. Implementation of common sampling techniques for nerves and ganglia across species provides increased rigor and reproducibility. TEM studies promote an improved understanding of functional studies and provide critical ultrastructural information for the development of new neuromodulation strategies.Support or Funding InformationSPARC/NIH 1OT2OD026585‐01 and Dr Miriam and Sheldon G. Adelson Medical Research Foundation (AMRF)

Chaotic driven maps: Non-stationary hyperbolic attractor and hyperchaos

In this paper we study simple examples of non-autonomous maps having different changing in time chaotic attractors. We present the definition of non-stationary hyperbolic attractor of the driven maps. We rigorously prove the existence of non-stationary hyperbolic attractor in 2D driven map and introduce a hyperchaotic attractor for autonomous 3D map of master-slave structure. Our analysis is based on the auxiliary systems approach and the construction of invariant cones.

Coexisting seismic behavior of transform faults revealed by high-resolution bathymetry

AbstractTransform faults have anomalously low rates of seismicity, but it’s not clear whether this reflects persistent earthquake-generating fault patches surrounded by creep, or the presence of creep and earthquakes at different times along the same patch. We use new, autonomous underwater vehicle high-resolution seafloor mapping to image the morphology of and offsets along transform fault segments in the Gulf of California, offshore Mexico. Fault zone structure imaged in this study shows evidence for the initiation and cessation of activity along individual fault splays over geologic time. A series of six identically offset depositional fans evidence 21–23 m of slip along the main transform fault, which could not have been produced by a single earthquake given the length of the transform. Rather, the lack of smaller-magnitude offsets indicates synchronous deposition and an absence of multiple slope failure–inducing earthquakes, which is consistent with the idea that creep and/or small-magnitude events occur asynchronously with large earthquakes in the slip history of a given transform fault segment.

Ideal Angular Orientation of Selected 64-Channel Multi Beam Lidars for Mobile Mapping Systems

Lidar technology is thriving nowadays for different applications mainly for autonomous navigation, mapping, and smart city technology. Lidars vary in different aspects and can be: multi beam, single beam, spinning, solid state, full 360 field of view FOV, single or multi pulse returns, and many other geometric and radiometric aspects. Users and developers in the mapping industry are continuously looking for new released Lidars having high properties of output density, coverage, and accuracy while keeping a lower cost. Accordingly, every Lidar type should be well evaluated for the final intended mapping aim. This evaluation is not easy to implement in practice because of the need to have all the investigated Lidars available in hand and integrated into a ready to use mapping system. Furthermore, to have a fair comparison; it is necessary to ensure the test applied in the same environment at the same travelling path among other conditions. In this paper, we are evaluating two state-of-the-art multi beam Lidar types: Ouster OS-1-64 and Hesai Pandar64 for mapping applications. The evaluation of the Lidar types is applied in a simulation environment which approximates reality. The paper shows the determination of the ideal orientation angle for the two Lidars by assessing the density, coverage, and accuracy and presenting clear performance quantifications and conclusions.

Realization of Autonomous Mapping and Path Planning in a Lower Limb Rehabilitation Robot Moving Platform

Map construction is a key technique for the indoor navigation and positioning, which is widely used in the robot mobile platform. This paper has designed and realized an autonomous mapping and path planning method for the lower limb rehabilitation robot moving platform. The method is completed by a Python program and functional packages in ROS. Simulations in Gazebo environment are carried out and the experiments oriented to the real scenarios are also presented. It is proved that the proposed method is very effective

Autonomous Exploration and Map Construction of a Mobile Robot Based on the TGHM Algorithm.

An a priori map is often unavailable for a mobile robot in a new environment. In a large-scale environment, relying on manual guidance to construct an environment map will result in a huge workload. Hence, an autonomous exploration algorithm is necessary for the mobile robot to complete the exploration actively. This study proposes an autonomous exploration and mapping method based on an incremental caching topology–grid hybrid map (TGHM). Such an algorithm can accomplish the exploration task with high efficiency and high coverage of the established map. The TGHM is a fusion of a topology map, containing the information gain and motion cost for exploration, and a grid map, representing the established map for navigation and localization. At the beginning of one exploration round, the method of candidate target point generation based on geometry rules are applied to extract the candidates quickly. Then, a TGHM is established, and the information gain is evaluated for each candidate topology node on it. Finally, the node with the best evaluation value is selected as the next target point and the topology map is updated after each motion towards it as the end of this round. Simulations and experiments were performed to benchmark the proposed algorithm in robot autonomous exploration and map construction.

Autonomous State Estimation and Mapping in Unknown Environments With Onboard Stereo Camera for Micro Aerial Vehicles

Industrial micro aerial vehicles (MAVs) with robotic manipulators have numerous applications in search and rescue tasks that reduce risks to human beings. However, such tasks distinctly require MAVs to have the capability of real-time autonomous navigation only with onboard sensors, especially in GPS-denied applications. This article introduces a new approach to onboard vision-based autonomous state estimation and mapping for MAVs’ navigation in unknown environments. The algorithms run on board and do not need an external positioning system to assist autonomous navigation. The state estimator is developed to provide MAV's current pose on the basis of the extended Kalman filter by using image patch features. Inverse depth convergence monitoring and local bundle adjustment are utilized to improve the accuracy. The mapping algorithm for navigation is developed according to a real-time stereo matching method for three-dimensional perception. Finally, we have performed several experiments to demonstrate the effectiveness of the proposed approach.

Beltéri Wi-Fi jelerősség feltérképező mobil robot tervezése

A cikk egy autonóm Wi-Fi jelerősség feltérképező robot tervezését részletezi. Szó esik benne a Wi-Fi jelerősségének méréséről, a mérés során vizsgált paraméter tulajdonságairól és értelmezésének módjáról. A robot tervének részletezése előtt bemutatásra kerülnek a vele elvégzendő jelfeltérképezési folyamatok, a robot elvárt funkciói és azok megvalósítása. A cikk központi témájául szolgáló terv két nagyobb fejezetben van ismertetve. A publikáció első részében a robot fizikai felépítését alkotó fontosabb alkatrészek kerülnek bemutatásra, azok funkcióival, felhasználásuk okaival és módjával, valamint előnyeikkel és hátrányaikkal. A második részben pedig a robot működéséhez szükséges programok és azok alapjai kerülnek részletezésre.

Hybrid SLAM modelling of autonomous robot with augmented reality device

This paper would focus on an innovative approach in simultaneous localization and mapping (SLAM) for autonomous robot navigation based on augmented reality (AR) technology. HoloLens as an advanced AR device using several innovative technologies, is selected for this Hybrid SLAM approach. The paper reveals that how this device can be used in advanced control for autonomous robot positioning, mapping and trajectory following. The HoloLens’ spatial mapping feature simplifies the SLAM problem considering as a hybrid algorithm combined with conventional EKF (Extended Kalman Filter). To convey the proposed hybrid approach, first a simulation model is established through EKF SLAM. second spatial mapping classes are introduced and applied in HoloLens. The result would illustrate the achieved map of environment by this algorithm.

An Indoor Global Localization Technique for Mobile Robots in Long Straight Environments

Scan matching methods have been widely applied in the fields of autonomous localization and mapping. However, in structured environments where feature differences are less significant, such as long straight corridors, conventional positioning algorithms often suffer from characteristic mismatching, resulting in lower accuracy. As such, a new global localization algorithm, based on environmental difference evaluation and correlation scan matching fusion, is proposed in this study. In this process, the surrounding space was evaluated using a priori understanding of the environment based on a linear fit. Corresponding evaluation and positioning results from correlation scan matching were then modified using dynamic selection and a posture updating strategy. The performance of the proposed technique was compared with other conventional methods using open datasets exhibiting long straight features and a series of tests conducted in a physical corridor. Results showed that the proposed algorithm could effectively improve localization accuracy in narrow environments. The translation and rotation absolute pose errors were reduced by an average of 27.29% and 25.82%, respectively, compared with a correlation matching approach that does not consider the surrounding geometry. These results suggest the proposed technique offers higher adaptability and positioning accuracy in narrow environments.

ROS Based Autonomous Robot with Open CV

In this project, we have designed and developed an autonomous robot that is powered by Robot Operating System (ROS). The capabilities of the robot include autonomous navigation, image tracking and mapping. OpenCV has been implemented in the on-board microprocessor to process the images that are captured by the general purpose webcams on the robot. A microcontroller has also been used to control the motors. The ultimate aim of this project is to develop a mobile robot capable of making its own decisions based on the images received.

Emerging Technologies and Coral Reef Conservation: Opportunities, Challenges, and Moving Forward

Coral reefs worldwide are declining at an accelerating rate due to multiple types of human impacts. Meanwhile, new technologies with applications in reef science and conservation are emerging at an ever-faster rate and are simultaneously becoming cheaper and more accessible. Technology alone cannot save reefs, but it can potentially help scientists and conservation practitioners study, mitigate, and even solve key challenges facing coral reefs. We examine how new and emerging technologies are already being used for coral reef science and conservation. Examples include drones, autonomous underwater vehicles (AUVs), 3D mapping and modelling tools, high-resolution and nano-satellite imagery, and a suite of monitoring and surveillance tools that are revolutionizing enforcement of sustainable reef fisheries. We argue that emerging technologies can play a pivotal role in tackling many of the critical issues facing coral reef conservation science and practice, but maximizing the impact of these technologies requires addressing several significant barriers. These barriers include lack of awareness of technologies and tools, prohibitive cost, lack of transferability across systems and/or scales, lack of technical expertise, and lack of accessibility. We discuss where analogous challenges have been overcome in another system and identify insights that can provide guidance for wise application of emerging technologies to coral reef science and conservation. Thoughtful consideration of, and adaptation to, these challenges will help us best harness the potential of emerging and future technological innovations to help solve the global coral reef crisis.

Autonomous Indoor Exploration Via Polygon Map Construction and Graph-Based SLAM Using Directional Endpoint Features

In this paper, a novel 2-D laser-based autonomous exploration approach for mobile robots is proposed, which is based on a novel polygon map construction approach and graph-based simultaneous localization and mapping (SLAM) with directional endpoint features. This approach is composed of three modules: graph-based SLAM using directional endpoint features, polygon map construction, and exploration. Different from existing approaches in the field of 2-D SLAM, the newly proposed 2-D graph-SLAM is based on 3-D “directional endpoint” features; on this basis, a well-known data structure “circular-doubly linked list” is applied to construct a novel polygon map for navigation. Note that it is efficient for circular-doubly linked list to initialize and update the polygon map. In addition, we propose a new information entropy calculation approach to quantify the entropy of the polygon map. Then for each candidate goal, we could obtain corresponding information gain and make next decision through collision detection. Comparative experimental results with respect to the well-known Gmapping and Karto SLAM are presented to show superior performance of the proposed graph-based SLAM. The autonomous exploration experiments in the office and hallway environments show the effectiveness of the proposed approach for robotic mapping and exploration tasks. Note to Practitioners —This paper is motivated by the challenges of autonomous exploration for mobile robots. We suggest a novel autonomous exploration approach through directional endpoint feature-based graph simultaneous localization and mapping (SLAM) and polygon map construction. This newly proposed approach does not require any artificial landmark or underlying occupancy grid map, which is easy to implement. The experiments are carried out in the office and hallway environments, including graph-based SLAM and autonomous exploration for mobile robots. The experimental results show the effectiveness of the proposed autonomous exploration framework. In the future research, we will address autonomous exploration in more challenging dynamic environments.

A Business Process Framework and Operations Map for Maritime Autonomous and Unmanned Shipping: MAUSOM

Maritime autonomous and unmanned shipping have considerable potential for cost-effective and safe cargo or passenger transport and other maritime operations. Such a manner of shipping will introduce operations chains with new roles and responsibilities. Widescale adoption requires an agreed upon overarching business process framework and operations map. Therefore, this paper introduces MAUSOM: the Maritime Autonomous and Unmanned Shipping Operations Map. Within the maritime industry and research community MAUSOM can be used as a shared blueprint for (re-)designing the operations processes and for defining the supporting functional capabilities. Furthermore, it structures the task allocation process to identify those tasks that can and those that (currently) cannot be allocated to software modules. Furthermore, it enables the definition of the data models needed for the overall system design. The MAUSOM methodology is based on the similar and successful enhanced Telecom Operations Map (eTOM) framework approach from the telecommunication sector [11]. MAUSOM uses Hierarchical Task Analysis (HTA), which has a long tradition as human factors’ functional decomposition method for engineering maritime operations. This paper describes MAUSOM, the benefits of its standardization and adoption for various stakeholders in the maritime shipping operations chain and addresses its development and deployment governance aspects.

Underwater Laser Scanner: Ray-Based Model and Calibration

Underwater 3-D perception is crucial for autonomous manipulation and mapping. Using a mirror-galvanometer system to steer a laser line projector and using triangulation with a camera it is possible to produce full 3-D perception, while avoiding moving the sensor with respect to the scene. This paper presents a ray-based model of a laser scanner and a calibration method for such a model. The model takes into account the distortion introduced to the system by the flat viewports used for both the laser and the camera. The paper also introduces the computation of elliptical cones as a way to synthesize the ray-based model and speed up the triangulation computation. The present study reports results obtained using the real sensor in a water tank to reconstruct objects and planes to assess the sensor's accuracy, as well as a comparison between the two triangulation methods (ray-based and elliptical cone).

Optimization Based Multi-View Coverage Path Planning for Autonomous Structure From Motion Recordings

The area of autonomous mapping and autonomous maintenance using unmanned robots has received much attention in recent years. However, usually only single-view coverage is ensured, which is not sufficient if a camera-based three-dimensional reconstruction is desired. If high precision is required for this reconstruction, computations are expensive and not real-time feasible, such that recording and reconstruction procedure must be executed consecutively. This calls for a reliable method for collecting a sufficient set of images during the recording procedure. In this letter, we present the first approach for multi-view coverage path planning, specifically targeted at an autonomous structure from motion data acquisition. For that purpose, we introduce an incremental, computational efficient update formula, that gives an upper bound on the expected reconstruction quality of observed points on the boundary of a structure. Using this quality estimate, we iteratively solve the coverage path-planning problem by greedily selecting a next-best-view as a solution to an optimal experimental design problem. Additional geometric and dynamic constraints ensure the admissibility of the solution. The correctness of our quality estimate and coverage path-planning approach are validated in the physics simulator Gazebo.