Artificial Landmarks Research Articles

Field Results of a Navigation Architecture with Timing Constraints

This paper presents results of field tests of a mobile robot controlled by a navigation architecture accounting for timing constraints in an indoor environment. Dependability properties characterize the effects of disturbances on the ability to successfully accomplish any assigned missions, described in terms of the stability of an equilibrium state identified with a goal location. The stability is analyzed using Contraction theory. A localization system based on artificial landmarks is used to obtain location estimates that enable the robot to autonomously cover large distances. Monte Carlo tests assess the architecture under different real environment conditions including recovering from disturbing events such as landmark detection failures, robot kidnapping, unexpected collisions, and changes in the density of obstacles in the environment. Tests include long-run missions of around 2900 m lasting for 4.5 hours.

A New Method For Indoor SLAM Based On Artificial Landmark

A mobile robot must be capable of localizing itself in unknown environments and constructing a map of the environments at the same time. Simultaneous localization and mapping (SLAM) is a challenging problem in indoor envi- ronments because GPS information isn't available. An algorithm is proposed in this paper for SLAM with vision sensors, which is designed by utilizing the artificial landmark MR code and integrating the FastSLAM algorithm, the method can decrease the time cost compared with particle filter and improve the accuracy of localization and mapping compared with natural landmarks. Experiments show the effectiveness and robustness of this algorithm.

소나 영상을 이용한 확률적 물체 인식 구조 기반 수중로봇의 위치추정

This paper proposes an underwater localization algorithm using probabilistic object recognition. It is organized as follows; 1) recognizing artificial objects using imaging sonar, and 2) localizing the recognized objects and the vehicle using EKF(Extended Kalman Filter) based SLAM. For this purpose, we develop artificial landmarks to be recognized even under the unstable sonar images induced by noise. Moreover, a probabilistic recognition framework is proposed. In this way, the distance and bearing of the recognized artificial landmarks are acquired to perform the localization of the underwater vehicle. Using the recognized objects, EKF-based SLAM is carried out and results in a path of the underwater vehicle and the location of landmarks. The proposed localization algorithm is verified by experiments in a basin.

Intelligent Indoor Mobile Robot Navigation Using Stereo Vision

Majority of the existing robot navigation systems, which facilitate the use of laser range finders, sonar sensors or artificial landmarks, has the ability to locate itself in an unknown environment and then build a map of the corresponding environment. Stereo vision, while still being a rapidly developing technique in the field of autonomous mobile robots, are currently less preferable due to its high implementation cost. This paper aims at describing an experimental approach for the building of a stereo vision system that helps the robots to avoid obstacles and navigate through indoor environments and at the same time remaining very much cost effective. This paper discusses the fusion techniques of stereo vision and ultrasound sensors which helps in the successful navigation through different types of complex environments. The data from the sensor enables the robot to create the two dimensional topological map of unknown environments and stereo vision systems models the three dimension model of the same environment.

Artificial landmark-based underwater localization for AUVs using weighted template matching

This paper deals with vision-based localization techniques in structured underwater environments. For underwater robots, accurate localization is necessary to perform complex missions successfully, but few sensors are available for accurate localization in the underwater environment. Among the available sensors, cameras are very useful for performing short-range tasks despite harsh underwater conditions including low visibility, noise, and large areas of featureless scene. To mitigate these problems, we design artificial landmarks to be utilized with a camera for localization, and propose a novel vision-based object detection technique and apply it to the Monte Carlo localization (MCL) algorithm, a map-based localization technique. In the image processing step, a novel correlation coefficient using a weighted sum, multiple-template-based object selection, and color-based image segmentation methods are proposed to improve the conventional approach. In the localization step, to apply the landmark detection results to MCL, dead-reckoning information and landmark detection results are used for prediction and update phases, respectively. The performance of the proposed technique is evaluated by experiments with an underwater robot platform and the results are discussed.

Monocular SLAM for Autonomous Robots with Enhanced Features Initialization

This work presents a variant approach to the monocular SLAM problem focused in exploiting the advantages of a human-robot interaction (HRI) framework. Based upon the delayed inverse-depth feature initialization SLAM (DI-D SLAM), a known monocular technique, several but crucial modifications are introduced taking advantage of data from a secondary monocular sensor, assuming that this second camera is worn by a human. The human explores an unknown environment with the robot, and when their fields of view coincide, the cameras are considered a pseudo-calibrated stereo rig to produce estimations for depth through parallax. These depth estimations are used to solve a related problem with DI-D monocular SLAM, namely, the requirement of a metric scale initialization through known artificial landmarks. The same process is used to improve the performance of the technique when introducing new landmarks into the map. The convenience of the approach taken to the stereo estimation, based on SURF features matching, is discussed. Experimental validation is provided through results from real data with results showing the improvements in terms of more features correctly initialized, with reduced uncertainty, thus reducing scale and orientation drift. Additional discussion in terms of how a real-time implementation could take advantage of this approach is provided.

Autonomous Position Estimation of a Mobile Node Based on Landmark and Localization Sensor

This study proposes an efficient position estimation method for localizing a mobile node in indoor environment. Although several conventional methods have been successfully applied for position estimation, they have some drawbacks such as low extendibility in an indoor space, intensive computation, and estimation errors. We propose a precise estimation approach based on a localization sensor and artificial landmarks. In our approach, a mobile node autonomously measures the location of landmarks attached to the ceiling with a localization sensor while moving across the landmarks and building a landmark map. And then, the node estimates its location under the ceiling using the map. In this process, we use a landmark histogram and a Kalman filter to reduce estimation errors. Several experiments performed using a mobile robot successfully demonstrated the feasibility of our proposed approach.

소나 영상 기반의 수중 물체 인식과 추종을 위한 구조 : Part 1. 소나 영상의 특성을 고려한 인공 표식물 설계 및 인식

소나 영상 기반의 수중 물체 인식과 추종을 위한 구조 : Part 1. 소나 영상의 특성을 고려한 인공 표식물 설계 및 인식

An Indoor Localization System for Mobile Robots Using an Active Infrared Positioning Sensor

Infrared (IR) sensors have been widely used as a distance measuring sensors to estimate the position of an object since they have short response time and high accura- cy. Therefore, we introduce the indoor localization system using an active infrared sensor and passive landmarks. However, there were several types of deviations from the actual values. We analyzed these errors and corrected them to develop the improved localization system.  Index Terms—infrared sensor, artificial landmark, multi- robot, indoor localization, intelligent space

이동로봇의 저비용 위치추정을 위한 효율적인 인공표식 배치기법

인공표식은 이동로봇의 위치추정에서 불확실성을 감소시키기 위해 널리 사용되어 왔다. 또한, 사용되는 인공표식의 수가 증가함에 따라 위치추정 비용이 증가하기 때문에, 인공표식의 효율적 배치를 위한 연구는 핵심적인 이슈중 하나로서 여겨져 왔다. 따라서 본 논문은 운동모델과 센서모델의 불확실성 특성을 고려함으로써 인공표식들을 효율적으로 배치하기 위한 방법을 제안한다. 운동모델과 센서모델은 서로 다른 불확실성 분포를 가지기 때문에, 최종 불확실성은 두 가지 불확실성의 효율적 조합을 통해 크게 감소될 수 있다. 제안한 기법의 유용성은 시뮬레이션 결과에 의해 입증된다. Artificial landmarks have been widely used for reducing the uncertainty in localization of a mobile robot. In addition, research for efficient placement of artificial landmarks has been considered as one of the fundamental issues since the cost of localization is increased with the number of used landmarks. Therefore, this paper proposes a method in which landmarks are efficiently placed by considering the uncertainty characteristics of the motion model and the sensor model. Because two models have different uncertainty distributions, the final uncertainty can be considerably reduced through their efficient combination. The usefulness of the proposed method is demonstrated by simulation results.

Active exploration using a scheme for autonomous allocation of landmarks

SUMMARYIn this paper, we focus on the unknown environments without artificial landmarks and features, such as disaster situations and polar regions. An approach to active exploration based on an on-line scheme for autonomous allocation of landmarks is proposed. Specifically, the robot carries along with itself some landmarks which are to be allocated during the exploration according to some heuristic rules. The utility of landmark allocation is analyzed and calculated. Then the active exploration is converted into a problem of multi-objective optimization. The objective function includes three weighted terms: the accuracy of localization and mapping, the coverage rate of the unknown environment and the utility of the allocated landmarks. By solving this optimization problem, control inputs of the robot are computed to guarantee that accurate localization, high-quality mapping and complete exploration can be achieved simultaneously. Moreover, supplementation and redundancy elimination of the allocated landmarks are executed to make a complete and non-redundant coverage for the environment. Finally, some landmarks, together with a device for allocating these landmarks, are developed. Both experiment and simulation results are presented to demonstrate the effectiveness of the proposed approach.

New Practical Landmarks to Determine Sigmoid Sinus Free Zones for Suboccipital Approaches

Literature defines the landmarks to identify the courses and locations of the transverse and sigmoid sinuses on the outer surface of the skull and inner surface of the scalp. These natural landmarks may only be helpful after skin incision and are inadequate to determine the length and size of the skin incision. Still, there is a need to identify palpable landmarks easily to determine the ideal location to open the initial burr hole before an operation. Twenty-eight dried adult human skulls and 2 cadavers were evaluated. The zygomatic root, the inion, and the mastoid process were identified on the external, and the grooves for sigmoid and transverse sinuses, on the internal surfaces. The distances between the 3 landmarks and the midpoints, and the shortest distances of the midpoints to the border of the groove for sigmoid sinus and groove for transverse sinus were measured. Statistically significant differences were evaluated for both sides. Based on the measurements, the defined "artificial landmarks" can be considered safe points that involve no vascular structures and may be used to perform the initial burr hole during posterolateral approaches. Identification of the midpoints and palpation of the defined landmarks easily before the operation render the study feasible and practical unlike with natural landmarks. To avoid venous injury, the midpoints of mastoid-inion line and zygomatic root-inion line can be used safely in skin incision during posterior fossa approaches and craniotomy.

Self-localisation of indoor mobile robots using multi-hypotheses and a matching algorithm

This paper proposes a new, fast and computationally light weight methodology to pinpoint a robot in a structured scenario.The localisation algorithm performs a tracking routine to pinpoint the robot’s pose as it moves in a known map, without the need for preparing the environment, with artificial landmarks or beacons. To perform such tracking routine, it is necessary to know the initial position of the vehicle. This paper describes the tracking routine and presents a solution to pinpoint that initial position in an autonomous way, using a multi-hypotheses strategy.This paper presents experimental results on the performance of the proposed method applied in two different scenarios: (1) in the Middle Size Soccer Robotic League (MSL), using artificial vision data from an omnidirectional robot and (2) in indoor environments using 3D data from a tilting Laser Range Finder of a differential drive robot (called RobVigil).This paper presents results comparing the proposed methodology and an Industrial Positioning System (the Sick NAV350), commonly used to locate Autonomous Guided Vehicles (AGVs) with a high degree of accuracy in industrial environments.

Diel leaf growth of soybean: a novel method to analyze two-dimensional leaf expansion in high temporal resolution based on a marker tracking approach (Martrack Leaf).

BackgroundWe present a novel method for quantitative analysis of dicot leaf expansion at high temporal resolution. Image sequences of growing leaves were assessed using a marker tracking algorithm. An important feature of the method is the attachment of dark beads that serve as artificial landmarks to the leaf margin. The beads are mechanically constricted to the focal plane of a camera. Leaf expansion is approximated by the increase in area of the polygon defined by the centers of mass of the beads surrounding the leaf. Fluctuating illumination conditions often pose serious problems for tracking natural structures of a leaf; this problem is circumvented here by the use of the beads.ResultsThe new method has been used to assess leaf growth in environmental situations with different illumination conditions that are typical in agricultural and biological experiments: Constant illumination via fluorescent light tubes in a climate chamber, a mix of natural and artificial illumination in a greenhouse and natural illumination of the situation on typical summer days in the field. Typical features of diel (24h) soybean leaf growth patterns were revealed in all three conditions, thereby demonstrating the general applicability of the method. Algorithms are provided to the entire community interested in using such approaches.ConclusionsThe implementation Martrack Leaf presented here is a robust method to investigate diel leaf growth rhythms both under natural and artificial illumination conditions. It will be beneficial for the further elucidation of genotype x environment x management interactions affecting leaf growth processes.

Vision-Based Indoor Localization Using Artificial Landmarks and Natural Features on the Ceiling with Optical Flow and a Kalman Filter

This paper proposes a vision-based indoor localization method for autonomous vehicles. A single upward-facing digital camera was mounted on an autonomous vehicle and used as a vision sensor to identify artificial landmarks and any natural corner features. An interest point detector was used to find the natural features. Using an optical flow detection algorithm, information related to the direction and vehicle translation was defined. This information was used to track the vehicle movements. Random noise related to uneven light disrupted the calculation of the vehicle translation. Thus, to estimate the vehicle translation, a Kalman filter was used to calculate the vehicle position. These algorithms were tested on a vehicle in a real environment. The image processing method could recognize the landmarks precisely, while the Kalman filter algorithm could estimate the vehicle’s position accurately. The experimental results confirmed that the proposed approaches can be implemented in practical situations.

Mapping Implementation for Multi-robot System with Glyph Localisation

Abstract An important prerequisite for creation of autonomous robot is the ability to create the map of the environment. Robots need to be able to construct a map of the environment and to use it for navigation. In this paper a multirobot mapping approach is proposed, where artificial landmarks (glyphs) are used for robot localisation and only simple sensors - bumper sensors - are available for obstacle detection. The proposed mapping system creates the map of the environment and allows changing the map rapidly in case of inaccurate robot position measurements or sensor errors. The system has a central server and robots use wireless network to communicate with the server. The mapping implementation takes into account the fact that the network has limited bandwidth and only limited amount of data can be sent.

Single Robot Localisation Approach for Indoor Robotic Systems through Integration of Odometry and Artificial Landmarks

Abstract we present an integrated approach for robot localization that allows to integrate for the artificial landmark localization data with odometric sensors and signal transfer function data to provide means for different practical application scenarios. The sensor data fusion deals with asynchronous sensor data using inverse Laplace transform. We demonstrate a simulation software system that ensures smooth integration of the odometry-based and signal transfer - based localization into one approach.

Visual Appearance-Based Unmanned Vehicle Sequential Localization

Localizationis of vital importance for an unmanned vehicle to drive on the road. Most of the existing algorithms are based on laser range finders, inertial equipment, artificial landmarks, distributing sensors or global positioning system(GPS) information. Currently, the problem of localization with vision information is most concerned. However, vision-based localization techniquesare still unavailable for practical applications. In this paper, we present a vision-based sequential probability localization method. This method uses the surface information of the roadside to locate the vehicle, especially in the situation where GPS information is unavailable. It is composed of two step, first, in a recording stage, we construct a ground truthmap with the appearance of the roadside environment. Then in an on-line stage, we use a sequential matching approach to localize the vehicle. In the experiment, we use two independent cameras to observe the environment, one is left-orientated and the other is right. SIFT features and Daisy features are used to represent for the visual appearance of the environment. The experiment results show that the proposed method could locate the vehicle in a complicated, large environment with high reliability.

MOBILE ROBOT SELF-PLANNING AND NAVIGATION BASED ON ARTIFICIAL LANDMARK LOCALIZATION METHOD AND BINOCULAR STEREO VISION

This paper describes design of artificial landmark based on colour model used for Self-planning in unstructured environment to a robot for its movement. This method provides less error in estimation when compared to existing methods. This project is an investigation into building a system which visually detects artificial landmarks to determine the landmarks within a location, decipher their position within that location and track the landmark throughout the location using Binocular stereo vision.

Robust and Fast Algorithm for Artificial Landmark Detection in an Industrial Environment

This paper describes a solution to detect and gather information on artificial landmarks placed in an industrial floor. This solution is composed of an observation module (artificial vision plus a chamber for light conditioning) and a fast algorithm for detecting and extracting landmarks. It is applicable with two types of landmarks, which provide useful information and in the future the solution may be applied in Autonomous Guided Vehicles (AGVs) for locating or path following. The execution time and accuracy results of the detection and extraction algorithm are presented in this paper, when applied in landmarks in good and degraded conditions. 1