Robot Localization Method Research Articles

Mobile Robot Self-Localization System Using Single Webcam Distance Measurement Technology in Indoor Environments

A single-webcam distance measurement technique for indoor robot localization is proposed in this paper. The proposed localization technique uses webcams that are available in an existing surveillance environment. The developed image-based distance measurement system (IBDMS) and parallel lines distance measurement system (PLDMS) have two merits. Firstly, only one webcam is required for estimating the distance. Secondly, the set-up of IBDMS and PLDMS is easy, which only one known-dimension rectangle pattern is needed, i.e., a ground tile. Some common and simple image processing techniques, i.e., background subtraction are used to capture the robot in real time. Thus, for the purposes of indoor robot localization, the proposed method does not need to use expensive high-resolution webcams and complicated pattern recognition methods but just few simple estimating formulas. From the experimental results, the proposed robot localization method is reliable and effective in an indoor environment.

Localization of Outdoor Mobile Robots Using Curb Features in Urban Road Environments

Urban road environments that have pavement and curb are characterized as semistructured road environments. In semistructured road environments, the curb provides useful information for robot navigation. In this paper, we present a practical localization method for outdoor mobile robots using the curb features in semistructured road environments. The curb features are especially useful in urban environment, where the GPS failures take place frequently. A curb extraction is conducted on the basis of the Kernel Fisher Discriminant Analysis (KFDA) to minimize false detection. We adopt the Extended Kalman Filter (EKF) to combine the curb information with odometry and Differential Global Positioning System (DGPS). The uncertainty models for the sensors are quantitatively analyzed to provide a practical solution.

Visual Localization of Mobile Robot Using Artificial Markers

The paper presents localization method for mobile robots with use of artificial markers deployed to an environment. A specialty of the markers is that the position and rotation of the camera relative to the marker can be determined from only one marker. Further in the paper, some experiments are described. The results prove that this system can be reliably employed in visual localization of mobile robots.

A Localization Method Based on Map-Matching and Particle Swarm Optimization

This paper presents a novel localization method for small mobile robots. The proposed technique is especially designed for the [email protected], a new robotic competition which is started in Lisbon in 2011. The real-time localization technique resorts to low-cost infra-red sensors, a map-matching method and an Extended Kalman Filter (EKF) to create a pose tracking system that performs well. The sensor information is continuously updated in time and space according to the expected motion of the robot. Then, the information is incorporated into the map-matching optimization in order to increase the amount of sensor information that is available at each moment. In addition, the Particle Swarm Optimization (PSO) relocates the robot when the map-matching error is high, meaning that the map-matching is unreliable and the robot gets lost. The experiments presented in this paper prove the ability and accuracy of the presented technique to locate small mobile robots for this competition. Extensive results show that the proposed method presents an interesting localization capability for robots equipped with a limited amount of sensors, but also less reliable sensors.

Active Visual Method for Mobile Robots Localization using Double Freedom Pan-Title

For a monocular camera-based mobile robot system,we propose the active visual method for robot localization,based on the double freedom pan-tilt.First,the kinematic model of the mobile robot system of monocular vision is given.Secondly,by controlling the pitch and yaw angles of pan-tilt,the camera optical axis always points to the target geometry center in order to solve the field of view problem.Coordinate system relative pose solution method which based on dual quaternion avoids calculating the singularity and improves the positioning accuracy of mobile robots.Finally,according to pan-tilts feedback angles,extrinsic parameters of the camera,the pose and the position of the camera,the position vector and pose vector of the mobile robot are obtained.

Evaluating different landmark positioning systems within the RIDE architecture

Mobile robots operating in the real world need a very reliable localization system to navigate autonomously for long periods of time. Numerous methods for indoor mobile robot localization have been developed. However, an affordable system covering all environments and situations is not yet available. Therefore, it is very important for mobile robot application developers to be aware of the operation and limitations of the different localization systems in order to obtain the best performance for each case. This paper evaluates two indoor localization systems that are integrated in the RIDE architecture: a commercial (Hagisonic StarGazer) and a low cost localization system based on the popular Wii remote control (WiiMote) with different tag distributions were evaluated. Characteristics that were tested include precision, accuracy, reliability, cost and immunity to interference.

High Precision GNSS Guidance for Field Mobile Robots

In this paper, we discuss GNSS (Global Navigation Satellite System) guidance for field mobile robots. Several GNSS systems and receivers, as well as multiple measurement methods and principles of GNSS systems are examined. We focus mainly on sources of errors and investigate diverse approaches for precise measuring and effective use of GNSS systems for real-time robot localization. The main body of the article compares two GNSS receivers and their measurement methods. We design, implement and evaluate several mathematical methods for precise robot localization.

An Improved Method for Mobile Robot Localization Based on Passive RFID System

RFID technology has been widely used in mobile robot positioning system for its unique advantages. RFID tags store their unique positions which are placed on the ceiling or the floor. The mobile robot carries a RFID reader which reads the RFID tags to position itself. In this paper, a new method for mobile robot localization is proposed, and the equations to calculate the position of the mobile robot are given. Finally, the experiment results show that compared to conventional positioning method, the proposed method can effectively improve the positioning accuracy of the mobile robot.

The Localization Method of Active Olfaction Robot and Experimental Study

An active olfaction implementation scheme based on zigzag search algorithm using a wheeled mobile robot and the experimental verification are put forward. Firstly, the hardware structure of the active olfaction robot is setup. Secondly, the Zigzag-based robot active olfaction dynamic system implementation procedure in the simulated environment is presented, including plume finding, plume tracking and odor source declaration, and simulation results validate the proposed search scheme. The entire system was used under steady wind condition to locate the ethanol odor source. Choice 15m as the start distance, and after 10 times locating, the error is less than 5% and the experimental results prove that the system recognition is accuracy.

On accurate localization and uncertain sensors

The necessity of accurate localization in mobile robotics is obvious—if a robot does not know where it is, it cannot navigate accurately and reach goal locations. Robots learn about their environment via sensors. Small robots require small, efficient, and, if they are to be deployed in large numbers, inexpensive sensors. The sensors used by robots to perceive the world are inherently inaccurate, providing noisy, erroneous data, or even no data at all. Combined with estimation error due to imperfect modeling of the robot, there are many obstacles to successfully localizing in the world. Sensor fusion is used to overcome these difficulties—combining the available sensor data to derive a more accurate pose estimation for the robot. A feeling of “ready-fire-aim'' pervades the discipline—filters are chosen on little to no information, and new filters are simply tested against a few peers and claimed as superior to all others. This is folly—the most appropriate filter is seldom the newest. This article provides an overview and in-depth tutorial of all modern robot localization methods and thoroughly discusses their strengths and weaknesses to assist a robot researcher in the task of choosing the most appropriate filter for their task. © 2012 Wiley Periodicals, Inc.

Hybrid adaptive differential evolution for mobile robot localization

This paper introduces a new evolutionary optimization algorithm named hybrid adaptive differential evolution (HADE) and applies it to the mobile robot localization problem. The behaviour of evolutionary algorithms is highly dependent on the parameter selection. This algorithm utilizes an adaptive method to tune the mutation parameter to enhance the rate of convergence and eliminate the need for manual tuning. A hybrid method for mutation is also introduced to give more diversity to the population. This method which constantly switches between two mutation schemes guarantees a sufficient level of diversity to avoid local optima. We use a well-known test set in continuous domain to evaluate HADE's performance against the standard version of differential evolution (DE) and a self-adaptive version of the algorithm. The results show that HADE outperforms DE and self-adaptive DE in three of four benchmarks. Moreover, we investigate the performance of HADE in the well-known localization problem of mobile robots. Results show that HADE is capable of estimating the robot's pose accurately with a decreased number of individuals needed for convergence compared with DE and particle swarm optimization methods. Comparative study exposes HADE algorithm as a competitive method for mobile robot localization.

Outdoor Localization Using Stereo Vision Under Various Illumination Conditions

We present a mobile robot localization method using only a stereo camera. Vision-based localization in outdoor environments is a challenging issue because of extreme changes in illumination. To cope with varying illumination conditions, we use two-dimensional occupancy grid maps generated from three-dimensional point clouds obtained by a stereo camera. Furthermore, we incorporate salient line segments extracted from the ground into the grid maps. The grid maps are not significantly affected by illumination conditions because occupancy information and salient line segments can be robustly obtained. On the grid maps, a robot's poses are estimated using a particle filter that combines visual odometry and map matching. We use edge-point-based stereo simultaneous localization and mapping to obtain simultaneously occupancy information and robot ego-motion estimation. We tested our method under various illumination and weather conditions, including sunny and rainy days. The experimental results showed the effectiveness and robustness of the proposed method. Our method enables localization under extremely poor illumination conditions, which are challenging for even existing state-of-the-art methods.

Appearance-based mapping and localization for mobile robots using a feature stability histogram

The strength of appearance-based mapping models for mobile robots lies in their ability to represent the environment through high-level image features and to provide human-readable information. However, developing a mapping and a localization method using these kinds of models is very challenging, especially if robots must deal with long-term mapping, localization, navigation, occlusions, and dynamic environments. In other words, the mobile robot has to deal with environmental appearance change, which modifies its representation of the environment. This paper proposes an indoor appearance-based mapping and a localization method for mobile robots based on the human memory model, which was used to build a Feature Stability Histogram (FSH) at each node in the robot topological map. This FSH registers local feature stability over time through a voting scheme, and the most stable features were considered for mapping, for Bayesian localization and for incrementally updating the current appearance reference view in the topological map. The experimental results are presented using an omnidirectional images dataset acquired over the long-term and considering: illumination changes (time of day, different seasons), occlusions, random removal of features, and perceptual aliasing. The results include a comparison with the approach proposed by Dayoub and Duckett (2008) [19] and the popular Bag-of-Words (Bazeille and Filliat, 2010) [35] approach. The obtained results confirm the viability of our method and indicate that it can adapt the internal map representation over time to localize the robot both globally and locally.

Global Localization of Terminal Service Robot Based on PTZ Vision

According to environment characteristic of airport terminal, a simple and feasible global localization method of terminal service robot based on PTZ vision is presented. Terminal signs are searched intermittently along “S” trajectory and zoomed by PTZ camera. To sight them accurately, the PTZ vision behavior based on fuzzy logic controller is designed. Then the signs will be identified based on SIFT features, and the global location of the robot can be finally gotten according to their location information and geometric pattern. The validity and practicability of this method are confirmed by actual robot experiments.

Localization on an Underwater Robot Using Monte Carlo Localization Algorithm

The paper proposes a localization method of an underwater robot using Monte Carlo Localization(MCL) approach. Localization is one of the fundamental basics for autonomous navigation of an underwater robot. The proposed method resolves the problem of accumulation of position error which is fatal to dead reckoning method. It deals with uncertainty of the robot motion and uncertainty of sensor data in probabilistic approach. Especially, it can model the nonlinear motion transition and non Gaussian probabilistic sensor characteristics. In the paper, motion model is described using Euler angles to utilize the MCL algorithm for position estimation of an underwater robot. Motion model and sensor model are implemented and the performance of the proposed method is verified through simulation.

SURF와 Particle filter를 이용한 이동 로봇의 위치 추정

본 논문에서는 SURF와 Particle filter를 이용한 이동로봇의 위치 추정 방법을 제안한다. 제안한 방법은 다음과 같다: 먼저, 이동 로봇의 위치를 찾기 위해 SURF 알고리증을 이용하여 카메라로부터 획득한 영상을 분석한다. 두 번째, 획득한 영상으로부터 이동로봇의 상대적인 위치를 알기 위해 이동로봇에 설치되어 있는 초음파 센서를 이용하여 주변 환경과의 거리를 측정한다. 그리고 측정된 센서 값들을 기반으로 하여 이동 로봇의 위치를 추정하는데 있어서 오차를 줄이고자 위치 추정에 많이 사용되는 Particle filter를 이용하여 이동 로봇의 위치를 추정한다. 마지막으로, 본 논문에서 제안한 방법은 실험을 통해 그 응용 가능성을 증명한다. In this paper, we propose the localization method of mobile robot using SURF(Speeded-Up Robust Features) and Particle filter. The proposed method is as follows: First, we seek the Landmark from the obtained image using SURF in order to find the first rigorous position of mobile robot. Second, we obtain the distance from obstacles using ultrasonic sensors in order to create the relative position of mobile robot. And then, we estimate the localization of mobile robot using Particle filter about movement of mobile robot. Finally, we show the feasibility of the proposed method through some experiments.

Mobile Robot Localization Using Biased Chirp-Spread-Spectrum Ranging

In this paper, we propose a method of mobile robot localization based on chirp-spread-spectrum (CSS) ranging. By using the CSS system, the distances between a mobile robot and CSS nodes fixed at known coordinates can be measured according to the time of flight of radio frequency signals. Based on the measured distances, the coordinates of a mobile robot can be calculated by the method of trilateration. To deal with measurement noise, an extended Kalman filter (EKF) can be applied to estimate the coordinate of the mobile robot. These measured distances, however, are not only noisy but also biased. Therefore, the estimated coordinates of the mobile robot represent inconsistent values. To solve the problem of bias, we define a scaling factor, which corresponds to the change of the magnitude of a measured distance vector that is due to biases. Based on the scaling factor, we develop a new biased measurement model and apply the EKF to our model for estimating the coordinates of a mobile robot. Through localization experiments, we evaluate the performance of the proposed algorithm.

Localization and tracking algorithm for mobile robot based on wireless sensor network

PurposeThe purpose of this paper is to describe a low‐cost mobile robot localization and navigation system based on wireless sensor network (WSN). This can be used in extreme environments such as mines, hostile areas, and other places where static nodes cannot be deployed directly.Design/methodology/approachThe distance between robot and beacon nodes is estimated by received signal strength indicator (RSSI). The adaptive Kalman filter algorithm is used to eliminate noise from the RSSI signal. Then, the paper gets the mobile robot's status information which includes position, velocity, and acceleration. In order to improve the real‐time performance of systems and reduce the computation overhead of robot's CPU, the paper chooses a beacon node by some certain rules to implement the localization program. The robot and beacon nodes use the CC2430 as the communication and data‐processing unit.FindingsThe experiment shows it has the characteristics of good position precision, is easily realized, and inexpensive.Research limitations/implicationsThat the system needs so many beacon nodes, is the main limitation.Practical implicationsA very useful and low‐cost method for mobile robot localization and navigation.Originality/valueThe innovative aspect of the paper is that it attempts an analysis of the localization system based on WSN, by attempting to analyze and explain the model of localization system which includes robot and beacon nodes.

Robust mobile robot localization in highly non-static environments

In this paper, we propose a robust pose tracking method for mobile robot localization with an incomplete map in a highly non-static environment. This algorithm will work with a simple map that does not include complete information about the non-static environment. With only an initial incomplete map, a mobile robot cannot estimate its pose because of the inconsistency between the real observations from the environment and the predicted observations on the incomplete map. The proposed localization algorithm uses the approach of sampling from a non-corrupted window, which allows the mobile robot to estimate its pose more robustly in a non-static environment even when subjected to severe corruption of observations. The algorithm sequence involves identifying the corruption by comparing the real observations with the corresponding predicted observations of all particles, sampling particles from a non-corrupted window that consists of multiple non-corrupted sets, and filtering sensor measurements to provide weights to particles in the corrupted sets. After localization, the estimated path may still contain some errors due to long-term corruption. These errors can be corrected using nonlinear constrained least-squares optimization. The incomplete map is then updated using both the corrected path and the stored sensor information. The performance of the proposed algorithm was verified via simulations and experiments in various highly non-static environments. Our localization algorithm can increase the success rate of tracking its pose to more than 95% compared to estimates made without its use. After that, the initial incomplete map is updated based on the localization result.

천정부착 랜드마크 위치와 에지 화소의 이동벡터 정보에 의한 이동로봇 위치 인식

A new indoor mobile robot localization method is presented. Robot recognizes well designed single color landmarks on the ceiling by vision system, as reference to compute its precise position. The proposed likelihood prediction based method enables the robot to estimate its position based only on the orientation of landmark.The use of single color landmarks helps to reduce the complexity of the landmark structure and makes it easily detectable. Edge based optical flow is further used to compensate for some landmark recognition error. This technique is applicable for navigation in an unlimited sized indoor space. Prediction scheme and localization algorithm are proposed, and edge based optical flow and data fusing are presented. Experimental results show that the proposed method provides accurate estimation of the robot position with a localization error within a range of 5 cm and directional error less than 4 degrees.