Application of Kalman filter on mobile robot self-localization

Self-localization is a fundamental requirement for the mobile robot. Robot usually contains a large number of dif- ferent sensors, which provide the information of robot localization, and all the sensor information should be considered for the optimal location. Kalman filter is efficient to realize the information fusion. Used as an efficient sensor fusion algorithm, Kalman filter is an advanced filtering technique which can reduce errors of the position and orientation of the sensors. Kalman filter has been paied much attention to robot automation and solutions to solve uncertainties such as robot localization, navigation, following, tracking, motion control, estimation and prediction. The paper briefly describes Kalman filter theory, and establishes a simple mathematical model based on muti-sensor mobile robot. Meanwhile, Kalman filter is used in robot self-localization by simulations, and it is demonstrated by simulations that Kalman filter is effective.

Using self-location to calibrate the errors of observer positions for source localization

The uncertainty of observers＇ positions can lead to significantly degrading in source localization accuracy. This pa-per proposes a method of using self-location for calibrating the positions of observer stations in source localization to reduce the errors of the observer positions and improve the accuracy of the source localization. The relative distance measurements of the two coordinative observers are used for the linear minimum mean square error （LMMSE） estimator. The results of computer si-mulations prove the feasibility and effectiveness of the proposed method. With the general estimation errors of observers＇ positions, the MSE of the source localization with self-location calibration, which is significantly lower than that without self-location calibra-tion, is approximating to the Cramer-Rao lower bound （CRLB）.

Hierarchical self-localization of underwater wireless sensor network nodes

The follow-up application of underwater wireless sensor network is influenced by accuracy of self-localization of nodes. The self-localization of nodes is discussed in this paper. First of all, nodes of underwater wireless sensor network are classified into several levels according to the accuracy of position of nodes and the levels are from the first to the fifth in accordance with accuracy of nodes from high to low respectively. Secondly, the level of anchor nodes can be known by those unknown nodes from the information given by the anchor nodes themselves, At the same time the unknown nodes are able to be located in the area controlled by the first level of anchor nodes that are as the aggregation. Then the positioning algorithm is designed correspondingly in accordance with the accuracy level of nodes. Finally, the positioning algorithm is simulated and analyzed. The result shows that the unknown nodes can be located effectively by hierarchical control.

A Parallel Integration Method of Cooperative Target-Localization and Cooperative Self-localization

When a group of mobile agents track a target,they can locate themselves and the target in a cooperative manner.To maximize the group advantage,a parallel integration strategy of cooperative target-localization(CTL)and cooperative self-localization(CSL)is designed.Firstly,a global cost function containing the agents’positions and the target’s position is established.Secondly,along with the agents’positions being re-estimated during CTL,the Utransform is employed to propagate the error covariance of the position estimations among the agents.The simulation results show that,the proposal exploits more information for locating the target and the agents than the cases where CTL and CSL run separately,and the global optimal position estimations of the agents and the target are obtained.

Enhanced Bilinear Approach for Sensor Network Self-Localization Using Noisy TOF Measurements

This paper develops a new algorithm for sensor network self-localization, which is an enhanced version of the existing Crocco’s method in [11]. The algorithm explores the noisy time of flight (TOF) measurements that quantify the distances between sensor nodes to be localized and sources also at unknown positions. The newly proposed technique first obtains rough estimates of the sensor node and source positions, and then it refines the estimates via a least squares estimator (LSE). The LSE takes into account the geometrical constraints introduced by the desired global coordinate system to improve performance. Simulations show that the new technique offers superior localization accuracy over the original Crocco’s algorithm under small measurement noise condition.

Advanced Self-Localization and Navigation for Mobile Robots in Extraterrestrial Environments

Self-localization is one of the most important aspects for using mobile robots in unstructured environments. In this paper, the authors introduce a new approach for a self-localization and navigation unit for mobile platforms in extraterrestrial environments, based on the authors＂ successful results in self-localization of forestry machines on earth. The presented approach is developed from a highly modular concept, which allows a simple but efficient adaption to specific applications by just substituting some scenario dependent components. In this paper, the authors will explain the general concept and the terrestrial implementation so far. On this basis, the authors will demonstrate and discuss the necessary adaptions to the general concept in order to handle the different conditions on extraterrestrial surfaces.

Flat band localization due to self-localized orbital

We discover a new wave localization mechanism in a periodic wave system,which can produce a novel type of flat band and is distinct from the known localization mechanisms,i.e.,Anderson localization and flat band lattices.The first example we give is a designed electron waveguide(EWG)on 2DEG with special periodic confinement potential.Numerical calculations show that,with proper confinement geometry,electrons can be completely localized in an open waveguide.We interpret this flat band localization(FBL)phenomenon by introducing the concept of self-localized orbitals.Essentially,each unit cell of the waveguide is equivalent to an artificial atom,where the self-localized orbital is a special eigenstate with unique spatial distribution.These self-localized orbitals form the flat bands in the waveguide.Such self-localized orbital induced FBL is a general phenomenon of wave motion,which can arise in any wave systems with carefully engineered boundary conditions.We then design a metallic waveguide(MWG)array to illustrate that similar FBL can be readily realized and observed with electromagnetic waves.

Anchor Self-Localization Algorithm Based on UWB Ranging and Inertial Measurements

Localization systems utilizing Ultra-WideBand(UWB) have been widely used in dense urban and indoor environments. A moving UWB tag can be located by ranging to fixed UWB anchors whose positions are surveyed in advance. However, manually surveying the anchors is typically a dull and time-consuming process and prone to artificial errors. In this paper, we present an accurate and easy-to-use method for UWB anchor self-localization,using the UWB ranging measurements and readings from a low-cost Inertial Measurement Unit(IMU). The locations of the anchors are automatically estimated by freely moving the tag in the environment. The method is inspired by the Simultaneous Localization And Mapping(SLAM) technique used by the robotics community. A tightly-coupled Error-State Kalman Filter(ESKF) is utilized to fuse UWB and inertial measurements, producing UWB anchor position estimates and six Degrees of Freedom(6 DoF) tag pose estimates. Simulated experiments demonstrate that our proposed method enables accurate self-localization for UWB anchors and smooth tracking of the tag.

A vision-centered multi-sensor fusing approach to self-localization and obstacle perception for robotic cars

Most state-of-the-art robotic cars＇ perception systems are quite different from the way a human driver understands traffic environments. First, humans assimilate information from the traffic scene mainly through visual perception, while the machine perception of traffic environments needs to fuse information from several different kinds of sensors to meet safety-critical requirements. Second, a robotic car requires nearly 100% correct perception results for its autonomous driving, while an experienced human driver works well with dynamic traffic environments, in which machine perception could easily produce noisy perception results. In this paper, we propose a vision-centered multi-sensor fusing framework for a traffic environment perception approach to autonomous driving, which fuses camera, LIDAR, and GIS information consistently via both geometrical and semantic constraints for efficient self- localization and obstacle perception. We also discuss robust machine vision algorithms that have been successfully integrated with the framework and address multiple levels of machine vision techniques, from collecting training data, efficiently processing sensor data, and extracting low-level features, to higher-level object and environment mapping. The proposed framework has been tested extensively in actual urban scenes with our self-developed robotic cars for eight years. The empirical results validate its robustness and efficiency.

Robot Self-Location by Line Correspondences

This paper thoroughly investigates the problem of robot self-location by line correspondences. The original contributions are three-fold: (1) Obtain the necessary and sufficient condition to determine linearly the robot's pose by two line correspondences. (2) Show that if the space lines are vertical ones, it is impossible to determine linearly the robot's pose no matter how many line correspondences we have, and the minimum number of line correspondences is 3 to determine uniquely (but non-linearly) the robot's pose. (3) Show that if the space lines are horizontal ones, the minimum number of line correspondences is 3 for linear determination and 2 for non-linear determination of the robot's pose.

Concept Study of a Self-localization System for Snow-covered Roads Using a Four-layer Laser Scanner

Many advanced driver assistance systems have entered the market,and automated driving technologies have been developed.Many of them may not work in adverse weather conditions.A forward-looking camera,for example,is the most popular system used for lane detection but does not work for a snow-covered road.The present paper proposes a self-localization system for snowy roads when the roadsides are covered with snow.The system employs a four-layer laser scanner and onboard sensors and uses only pre-existing roadside snow poles provided for drivers in a snowy region without any other road infrastructure.Because the landscape greatly changes in a short time during a snowstorm and snow removal works,it is necessary to restrict the landmarks used for self-localization to tall objects,like snow poles.A system incorporating this technology will support a driver’s efforts to keep to a lane even in a heavy snowstorm.