Artificial Landmarks Research Articles

인공 랜드마크 기반의 이동 로봇 측위 기법

본 논문에서는 인공 랜드마크를 활용하여 구현한 두 가지의 실내 측위 시스템인 QR코드 기반 및 색상코드 기반의 측위 방법을 제안한다. QR코드 기반 방식은 위치 파악을 위한 참조 지점인 QR코드를 천장에 배치하고 로봇에 부착된 스마트폰이 QR코드를 감지해 위치를 계산한다. 색상코드 기반 방식은 로봇에 색상코드를 부착하여 천장의 카메라를 통해 위치 정보를 획득해 로봇의 위치를 측정한다.

Ballistic deployment of the Hayabusa2 artificial landmarks in the microgravity environment of Ryugu

Hayabusa2 deployed two artificial landmarks called “target markers (TMs)” on the asteroid Ryugu for autonomous landing control. To achieve precise deployment on target landing sites, the TMs were designed to dissipate kinetic energy and released near the asteroid surface (with an altitude of less than 40 m). This study evaluates the performance of the ballistic deployment in the actual microgravity environment by reconstructing the trajectories of the TMs from optical, altimetric, and radiometric data. In addition, based on the reconstructed trajectories, low-velocity impacts of the TMs on the surface of Ryugu are characterized with dynamical parameters, such as dissipated energy and a coefficient of restitution. The physical implications of the impact analysis are discussed in comparison with on-ground experimental data. Furthermore, the gravitational environment is investigated using the reconstructed trajectory data and a shape model of Ryugu, providing information on the local gravity anomaly. Consequently, this paper demonstrates the usefulness of deployable artificial landmarks for small-body landings and further offers insight on surface conditions and internal structures near the Hayabusa2 landing sites where samples of Ryugu were collected.

In vitro assessment of digital implant impressions accuracy using artificial landmarks

In vitro assessment of digital implant impressions accuracy using artificial landmarks

Границы и люди в Польше с XII по XVI столетия: пример Мазовии

В статье рассматривается возникновение и распространение искусственных локальных границ в регионе Мазовецкое воеводство, территория, расположенная в бассейне средней реки Вислы.Бедные почвы этого лесного региона задержали здесь сельскую и городскую колонизацию по сравнению с другими польскими землями. Несмотря на то, что первые упоминания о таких границах в Мазовии относительно ранние (датируются 1185 годом), периодические набеги прусских и ядовинских племен, а также литовцев в XIII и XIV веках остановили развитие колонизации. Соответственно, размежевание началось на рубеже тринадцатого и четырнадцатого веков, усилилось примерно с 1360 года и достигло апогея в XV веке. Эта практика была обусловлена созданием множества деревень и городов в соответствии с немецким законодательством, распадом семейных владений и демографическим ростом. В дополнение к естественным особенностям ландшафта (реки, ручьи, болота, холмы), а также к искусственным ориентирам, предназначенным для использования в качестве пограничных пунктов (дороги, мосты, церкви, часовни, колодцы, мельницы, языческие гробницы), широкое применение получили искусственные пограничные знаки (обычно насыпи из песка или камня, одиночные камни и Х-образные знаки). Пограничные знаки также играли роль носителей коллективной, местной памяти, благодаря своим конкретным именам или форме вырезанных / выгравированных знаков (кресты, гербы).
 История появления и распространения искусственных пограничных знаков и определения границ в Мазовии является частью истории преобразования, организации и обретения контроля над пространством средневековыми обществами

FloorVLoc: A Modular Approach to Floorplan Monocular Localization

Intelligent vehicles for search and rescue, whose mission is assisting emergency personnel by visually exploring an unfamiliar building, require accurate localization. With GPS not available, and approaches relying on new infrastructure installation, artificial landmarks, or pre-constructed dense 3D maps not feasible, the question is whether there is an approach which can combine ubiquitous prior map information with a monocular camera for accurate positioning. Enter FloorVLoc—Floorplan Vision Vehicle Localization. We provide a means to integrate a monocular camera with a floorplan in a unified and modular fashion so that any monocular visual Simultaneous Localization and Mapping (SLAM) system can be seamlessly incorporated for global positioning. Using a floorplan is especially beneficial since walls are geometrically stable, the memory footprint is low, and prior map information is kept at a minimum. Furthermore, our theoretical analysis of the visual features associated with the walls shows how drift is corrected. To see this approach in action, we developed two full global positioning systems based on the core methodology introduced, operating in both Monte Carlo Localization and linear optimization frameworks. Experimental evaluation of the systems in simulation and a challenging real-world environment demonstrates that FloorVLoc performs with an average error of 0.06 m across 80 m in real-time.

Robust Localization of the Mobile Robot Driven by Lidar Measurement and Matching for Ongoing Scene

Aiming at the localization problem of mobile robot in construction scenes, a hybrid localization algorithm with the adaptive weights is proposed, which can effectively improve the robust localization of mobile robot. Firstly, two indicators of localization accuracy and calculation efficiency are set to reflect the robustness of localization. Secondly, the construction scene is defined as an ongoing scene, and the robust localization of mobile robot is achieved by using the measurement of artificial landmarks and matching based on generated features. Finally, the experimental results show that the accuracy of localization is up to 8.22 mm and the most matching efficiency is controlled within 0.027 s. The hybrid localization algorithm that based on adaptive weights can realize a good robustness for tasks such as autonomous navigation and path planning in construction scenes.

Encoder-Camera-Ground Penetrating Radar Sensor Fusion: Bimodal Calibration and Subsurface Mapping

In this article, we report system and algorithmic developments for a sensing suite comprising a camera and a ground penetrating radar (GPR) with a wheel encoder designed for both surface and subsurface infrastructure inspection, which is a multimodal mapping task. To fuse different sensor modalities properly, we solve a novel GPR-camera calibration problem and a synchronization-challenged sensor fusion problem. First, we design a calibration rig, model the GPR imaging process, introduce a mirror to obtain the joint coverage between the camera and the GPR, and employ the maximum-likelihood estimator to estimate the relative pose between the camera and the GPR with error analysis. Second, we propose a data collection scheme using the customized artificial landmarks to synchronize camera images (temporally evenly spaced) and GPR/encoder data (spatially evenly spaced). We also employ pose graph optimization with location discrepancy as penalty functions to perform data fusion for 3-D reconstruction. We have tested our system in physical experiments. The results show that our system successfully fuses encoder-camera-GPR sensory data and accomplishes a metric 3-D reconstruction. Moreover, our sensor fusion approach reduces the end-to-end distance error from 6.4 to 0.7 cm in a real bridge inspection experiment if comparing to the counterpart that only uses encoder measurements.

Global optical flow-based estimation of velocity for multicopters using monocular vision in GPS-denied environments

Velocity estimation is a requisite for multicopters to guarantee flight stability and maneuverability. In this paper, a global optical flow-based approach is proposed for estimation of multicopter velocity using the off-the-shelf onboard sensors, including a downward-looking monocular camera, an Inertial Measurement Unit (IMU) and a sonar facing downwards in GPS-denied environments. The proposed method does not require any prior information or artificial landmark of the environment. First, a feature-based estimation method is proposed for richly textured surfaces while an image pyramids-based method extended for low-textured backgrounds to obtain the pixel motion. Then, combined with the estimated pixel motion, vision-based velocity estimation is developed, offering the visual observations. Furthermore, a classical linear Kalman filter for velocity estimation by fusing all the available information from sensors is derived. Finally, the proposed method is validated and demonstrated with a collection of synthetic data based on a Microsoft AirSim high-fidelity visual simulation platform as well as flight experiments with real data from a commercial DJI Matrice 100 quadcopter equipped with a Guidance visual sensing module. Simulation and experimental results indicate that the proposed method is capable of providing an accurate estimate of multicopter velocity in GPS-denied or confined environments.

Image-based autonomous navigation of Hayabusa2 using artificial landmarks: The design and brief in-flight results of the first landing on asteroid Ryugu

Hayabusa2 is an asteroid sample return mission carried out by the Japan Aerospace Exploration Agency. The spacecraft was launched in 2014 and arrived at the target asteroid Ryugu on June 27, 2018. During the 1.5-year proximity phase, several critical operations (including two landing/sampling operations) were successfully performed. They were based on autonomous image-based descent and landing techniques. This paper describes an imagebased autonomous navigation scheme of the Hayabusa2 mission using artificial landmarks named target markers (TMs). Its basic algorithm, and the in-flight results of the first touchdown and its rehearsal, are shown.

Design and Reconstruction of the Hayabusa2 Precision Landing on Ryugu

This research focuses on the design and reconstruction of the first touchdown of Hayabusa2 on the asteroid Ryugu. Because of the plethora of boulders, the landing operation required high accuracy to ensure spacecraft safety. This paper therefore investigates a pinpoint touchdown sequence using a retroreflective marker as an artificial landmark. First, the dynamical environment in the vicinity of Ryugu is explored for precise trajectory analyses; particularly, the irregular gravitational field around Ryugu is computed, which has an impact on the spacecraft motion. Based on the dynamics model, the touchdown trajectory used to land on the selected landing site with a radius of 2.8 m is designed. This process is followed by landing dispersion analyses, validating the feasibility of the touchdown sequence. As a result, the Hayabusa2 spacecraft successfully landed on the asteroid Ryugu on 21 February 2019. This paper further provides trajectory reconstruction results based on actual flight data, revealing that the achieved landing accuracy was 1 m. Consequently, this work demonstrates the performance of the proposed precision landing strategy and expands the capabilities of small-body missions.

카메라 기반의 인공표식 추적을 이용한 선행 차량 추종

카메라 기반의 인공표식 추적을 이용한 선행 차량 추종

UcoSLAM: Simultaneous localization and mapping by fusion of keypoints and squared planar markers

Simultaneous Localization and Mapping is the process of simultaneously creating a map of the environment while navigating in it. Most of the SLAM approaches use natural features (e.g. keypoints) that are unstable over time, repetitive in many cases or their number insufficient for a robust tracking (e.g. in indoor buildings). Other researchers, on the other hand, have proposed the use of artificial landmarks, such as squared fiducial markers, placed in the environment to help tracking and relocalization. This paper proposes a novel SLAM approach by fusing natural and artificial landmarks in order to achieve long-term robust tracking in many scenarios.Our method has been compared to the start-of-the-art methods ORB-SLAM2 [1], LDSO [2] and SPM-SLAM [3] in the public datasets Kitti [4], Euroc-MAV [5], TUM [6] and SPM [3], obtaining better precision, robustness and speed. Our tests also show that the combination of markers and keypoints achieves better accuracy than each one of them independently.

基于激光雷达回波强度的人工路标中心提取方法

基于激光雷达回波强度的人工路标中心提取方法

PSD – probabilistic algorithm for mobile robot 6D localization without natural and artificial landmarks based on 2.5D map and a new type of laser scanner in GPS-denied scenarios

This paper presents an approach to mobile robot 6D localization based on a 3D laser scanner in GPS-denied scenarios. Commonly, 6D localization using laser scanners is performed with the use of extraction and association of the features or by comparison of the whole scans (very often off-line) using the ICP algorithm or its modifications. However, in some unstructured non-urbanized rough terrain environments, feature extraction does not seem to be reliable enough. For such kind of environment, we present a new method to mobile robot localization in GPS-denied applications, called PSD (Point-to-Surfel Distance). Unlike state of the art localization methods using laser scanners, we consider every single laser scanner measurement as an observation and use Point-to-Surfel Distance for correction of position and orientation of the robot. Mobile robot localization is based on a specific representation of the terrain in the 2.5D surfel map (terrain height and inclination). The simulation tests compared our method using extended Kalman filter (EKF) and single laser scanner measurements with an up-to-date method using particle filter (PF) and comparing the scan lines with the reference map and with another method using Gaussian mixture maps. The tests confirmed that the proposed method provides satisfying results for GPS-denied scenarios in rough terrain without extractable landmarks and our method is thirty times faster than the PF method (serial implementation). KITTI benchmark tests and real terrain experiments confirmed its usefulness and advantages as well.

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.

Vision Based Position Control for Vertical Take-off and Landing (VTOL) Using One Singular Landmark

This project presents a vision based position control for Vertical Take-off and Landing (VTOL) to recognise a singular landmark for landing and take-off. Position control can provide safe flight and an accurate navigation. The circle landmark which used is an artificial landmark at known locations in an environment. Initially, a camera mounted on VTOL facing downward detecting landmarks in environments. A single circle used as landmark and VTOL will be control the position to reach the landmark. The images from the down-looking camera provided vision data to estimates position of VTOL from landmark. A mathematical method based on projective geometry using to locate VTOL on desired landmark from projected point in capture image. By compute the x-y coordinates of the VTOL with respect to landmark, height of camera above landmark will be obtained. VTOL can localize itself in known environment with pose estimation from landmark. The graphic user interface system (GUI) generate by MATLAB software is used to communicate with VTOL to control the VTOL position

3D Reconstruction and alignment by consumer RGB-D sensors and fiducial planar markers for patient positioning in radiation therapy

Background and objectivePatient positioning is a crucial step in radiation therapy, for which non-invasive methods have been developed based on surface reconstruction using optical 3D imaging. However, most solutions need expensive specialized hardware and a careful calibration procedure that must be repeated over time.This paper proposes a fast and cheap patient positioning method based on inexpensive consumer level RGB-D sensors. MethodsThe proposed method relies on a 3D reconstruction approach that fuses, in real-time, artificial and natural visual landmarks recorded from a hand-held RGB-D sensor. The video sequence is transformed into a set of keyframes with known poses, that are later refined to obtain a realistic 3D reconstruction of the patient. The use of artificial landmarks allows our method to automatically align the reconstruction to a reference one, without the need of calibrating the system with respect to the linear accelerator coordinate system. ResultsThe experiments conducted show that our method obtains a median of 1 cm in translational error, and 1∘of rotational error with respect to reference pose. Additionally, the proposed method shows as visual output overlayed poses (from the reference and the current scene) and an error map that can be used to correct the patient’s current pose to match the reference pose. ConclusionsA novel approach to obtain 3D body reconstructions for patient positioning without requiring expensive hardware or dedicated graphic cards is proposed. The method can be used to align in real time the patient’s current pose to a preview pose, which is a relevant step in radiation therapy.

Online Extrinsic Parameter Calibration for Robotic Camera–Encoder System

Cameras and encoders are widely used in mobile robots, and extrinsic parameter calibration of these sensors is crucial in practical performance. The existing approaches mainly rely on manual measurements, accurate computer-aided design (CAD) models, or carefully designed artificial landmarks. This paper presents a novel approach for automatically calibrating the extrinsic parameters of the robotic camera-encoder system. The approach first calculates a coarse estimation of the external parameters as well as the scale of the visual system, via free-scale hand-eye calibration of the camera and odometer. However, the coarse calibration result and scale of the visual system do not satisfy the accuracy requirement for further mobile robots' applications such as localization and navigation. A nonlinear optimization algorithm that considers both bundle adjustment and odometer measurement error functions is developed to refine the extrinsic parameter calibration result. This coarse-fine approach is computationally efficient and can achieve online calibration during the vehicle motion automatically. Furthermore, it can realize the calibration without using any artificial landmark or prior knowledge about CAD models. Finally, comparisons to other classic calibration approaches are performed with a series of simulations and experiments to illustrate the effectiveness of the approach.

Improvement of localization with artificial landmark for mobile manipulator

A method is presented for improving the localization accuracy of a mobile manipulator. The system consists of an industrial manipulator mounted on a mobile platform to perform fuselage drilling/riveting tasks in indoor environment. The localization is based on dual laser range finders mounted on the system and a number of artificial landmarks preset in the environment. Localization based on trilateration method is presented in this study. The adaptive unscented Kalman filter method is used to improve the accuracy of localization as well. The proposed method has been verified through experiments and the localization accuracy of mobile manipulator can be improved to 8 mm in most cases.

Automatic waterline detection and 3D reconstruction in model ship tests using stereo vision

The changes of the waterlines alongside ship hulls in model ship tests provide important information to estimate the resistance performance of the full-scale ships and to improve its hull form design. This Letter presents a systematic procedure for detecting a waterline and reconstructing its three-dimensional (3D) geometry using a stereo vision system. The proposed method utilises the information from non-distinctive geometric features and shapes characterised in the waterline around a model ship without using any specified colours or artificial landmarks. For reconstructing the 3D geometry of the detected waterline, a vertical stereo system and a direct disparity estimation method are employed along with the consideration of uncertainties in the stereo correspondence matching. Experimental results are shown to demonstrate the feasibility of the proposed method using an image dataset obtained in a model ship test in a towing tank environment.