The capability to navigate accurately is one of the features, that a mobile robot should have to be able to perform tasks autonomously. In a GPS/GNSS-denied environment, for example inside buildings, localization of a mobile platform is an especially challenging problem. In such cases, to provide a robot with the ability to determine its position and to analyze its surroundings, Simultaneous Localization and Mapping (SLAM) algorithms could be implemented. In the article, we present a SLAM system that uses a Kalman filter together with data gathered by a 2D LiDAR. Our approach applies the ICP algorithm to calculate the localization and employs clustering and shape recognition technics to build the map of the environment. The article contains a detailed description of the individual elements of the proposed SLAM solution. Furthermore, it presents the results of experiments during which the system was validated.
KEYWORDS: Radar, Complex systems, Algorithm development, Systems modeling, Modulation, Detection and tracking algorithms, Distance measurement, 3D modeling, Signal detection, Electronics
In this work, a two-dimensional analytical model of a multi-position radar system with ambiguous range measurements is elaborated and tested. In the proposed analytical model, properties of ambiguous measurements of fractional parts of relative unambiguity intervals in each radar are determined. Theorems are formulated defining the conditions of the unambiguous mapping of a target’s coordinates onto the aforementioned fractional parts, as well as a reverse unambiguous mapping of those fractional parts onto a two-dimensional vector of integers. The vector contains ranges from the target to pairs of radars composing the considered system. The theorems are based on a principle of mapping the measurements of fractional parts onto a multidimensional unit cube, and the interpretation of the total set of measurements as a multilayer structure of this cube. Moreover, each layer is a multidimensional hypersurface bounded by the cube faces, and the unambiguity conditions are reduced to the conditions that these layers do not intersect with each other. Based on the developed model and the formulated theorems, an algorithm is proposed for disclosing the ambiguities of the fractional parts mentioned, as well as for obtaining unambiguous estimates of the target coordinates. An example of a multi-position radar system and results of modeling chosen elements of the algorithm for disclosing ambiguities are also presented. The aims of further research are formulated, particularly regarding the synthesis of multiposition radar systems and the elaboration of an analytical model for systems for the localization of emission sources of periodic radio signals.
One of the most common methods of navigation data integration is the use of a Kalman filter. The paper presents a computer application facilitating testing of the properties of a Kalman filter, designed for a pedestrian or a robot positioning system using range measurements to several base stations. The description of the system is composed of a linear dynamics model and a non-linear observation model. Due to the non-linearity of the observation model, an extended Kalman filter (EKF) was chosen for the position estimation. A computer application was developed in the MATLAB® environment to test the filter properties and to support the process of choosing its parameters. The application gives an insight into the EKF operation and enables an assessment of its accuracy, depending on the assumed shape of the object trajectory, an assumed motion model, a number and locations of the base stations and the filter parameters. Chosen results of simulations are presented in the paper in the same form as they are seen in the GUI of our application. The application enables displaying the assumed trajectory of motion, its estimates from the EKF, as well as the components of estimated position and velocity errors. Additionally, it calculates and presents root-mean-squared (RMS) and maximal estimation errors. The application proved useful for testing the filter efficiency in various configurations. Due to its comfortable GUI, intuitive handling and wide range of possible tests, it can find its applications both in the research and the teaching.
A choice of positioning algorithm is a key issue when designing a navigation system. The paper presents an Unscented Kalman Filter (UKF) for a personal navigation system using range measurements between ultrawideband radio modules for positioning. The mentioned system is described by a linear dynamics model and a non-linear observation model, and therefore UKF can be successfully applied for its state estimation. The unscented Kalman filter is a suboptimal non-linear filtration algorithm, however, in contrast to algorithms such as EKF or LKF, it uses an unscented transformation (UT) as an alternative to a linearization of non-linear equations with the use of Taylor series expansion. The linearization considers only the first term of a Taylor series and its higher terms are omitted, which degrades estimation accuracy in highly non-linear systems. Lack of necessity of a linearization of the dynamics and observation models leads to a higher navigation system accuracy. It also facilitates an implementation of the algorithm as non-linear transformations of a set of deterministically chosen sigma points replaces calculations of Jacobian matrices. The paper presents a personal navigation system designed by the authors, describes its unscented Kalman filter used for a mobile user position estimation and contains chosen simulation results of the tests of the filter. Based on these data, efficiency of using this kind of filtration in non-linear navigation systems has been assessed.
The paper presents the results of the development of a method and an algorithm for the synthesis of optimal basic signalcode structures in the form of code binary sequences, with a minimum criterion for the side lobes of the periodic autocorrelation function of the indicated sequences. To develop this method, approaches based on set theory and number theory were used. The method is based on a discrete representation of the periodic autocorrelation function of sequences in the form of a system of equations defined on a set of integers, set-theoretic interpretation of the constituent parts of sequences, their integer transformations, mutual properties and relations. A number of transformations of the constituent parts of the sequences are developed, analytical expressions for the dependence of the sum modulus of the sequence elements on the sum of the side lobe levels of their periodic autocorrelation function are derived, and the necessary conditions for the existence of sequences are defined and formulated. The relationship between the parameters of the code binary sequence and the canonical representation of the Euler function on the dimension of the sequence is determined. Analytical relationships between the levels of the side lobes of the periodic autocorrelation function and the parameters of the transformed sequence structures are obtained. The criterion of the effectiveness of the developed method and the corresponding algorithm is the ratio of the number of all possible variants of code binary sequences of a given dimension to a quantity that is determined by the developed algorithm; an expression was obtained to estimate the indicated amount. This efficiency is confirmed by the results of simulation and experimental research. The developed method can be used for the creation of secretive noise-proof data transmission radio systems, remote control systems, radar, and communications.
The article contains an analysis of potential prospects of simultaneous localization and mapping (SLAM) algorithms application in imagery intelligence (IMINT). The first part of the paper presents a detailed description of the SLAM problem. Diverse solutions to the simultaneous localization and mapping problem and related research over the years are presented. The most promising of SLAM approaches are pointed out. To facilitate SLAM analysis, the problem is partitioned into three parts. First, various SLAM estimation techniques are characterized. A mathematical theory behind the usage of parametric filters, non-parametric filters, and least squares method is presented. Further, differences between SLAM algorithms are described in terms of various sensors used on-board SLAM platforms for the examination of the environment. The examination is commonly addressed as landmark extraction. A separate part of the paper discusses the image processing in SLAM. The last part of the SLAM analysis is dedicated to various approaches to map presentation. Further, the properties of SLAM techniques are characterized in terms of their potential benefits to IMINT. Prospects of increased efficiency, accuracy and safety of intelligence gathering process are discussed.
Simulative generation of images that would be registered by a photo camera installed on board UAV is a complex process, consisting of a number of geometric transformations and image processing operations. The article presents a method of creating such images based on the assumed UAV navigation parameters, i.e. geographical coordinates, altitude and angles of orientation. The operation of the proposed simulator is based on the use of equations related to the geometry of the perspective photo and the principle of operation of a digital camera. The simulator may be used as a generator of testing images, especially for research works related to creating and developing algorithms for vision-based navigation. It allows to simulate and save images for the whole preprogrammed flight trajectory. It supports also creating robust algorithms for processing erroneous data from integrated navigation systems. Comparing images obtained from an onboard camera with their simulated counterparts allows to determine synchronization errors, which is important for the appropriate operation of a vision-based navigation system.
The paper presents a synthetic information on a UAV-based radar terrain imaging system, its purpose, structure and working principle as well as terrain images obtained from flight experiments. A SAR technology demonstrator has been built as a result of a research project conducted by the Military University of Technology and WB Electronics S.A. under the name WATSAR. The developed system allows to obtain high resolution radar images, both in on-line and off-line modes, independently of the light conditions over the observed area. The software developed for the system allows to determine geographic coordinates of the imaged objects with high accuracy. Four LFM-CW radar sensors were built during the project: two for S band and two for Ku band, working with different signal bandwidths. Acquired signals were processed with the TDC algorithm, which allowed for a number of analyses in order to evaluate the performance of the system. The impact of the navigational corrections on a SAR image quality was assessed as well. The research methodology of the in-flight experiments of the system is presented in the paper. The projects results show that the developed system may be implemented as an aid to tactical C4ISR systems.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.