Sensor fusion and surrounding environment mapping for a mobile robot using a mixed extended Kalman filter (original) (raw)
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A basic requirement for an autonomous mobile robot is to localize itself with respect to a given coordinate system. In this regard two different op-erating conditions exist: structured and unstructured environment. The relative methods and algorithms are strongly influenced by the a priori knowledge on the environment where the robot operates. If the environ-ment is only partially known the localization algorithm needs a preliminary definition of a suitable environ-ment map. In this paper the localization problem is formulated in a stochastic setting and an Extended Kalman Filtering (EKF) approach is proposed for the integration of odometric, gyroscope and laser scanner measures. As gyroscopic measures are much more re-liable than the other ones, the localization algorithm gives rise to a nearly singular EKF. This problem is dealt with defining a lower order non singular EKF. I. INTRODUCTION Two different kinds of mobile robot localization exist: relative and absolute. The first one...
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A basic requirement for an autonomous mobile robot is its capability to elaborate the sensor measures to localize itself with respect to a coordinate system. To this purpose, the data provided by odometric and sonar sensors are here fused together by means of an extended Kalman filter. The performance of the filter is improved by an on line adjustment of the input and measurement noise covariances obtained by a suitably defined estimation algorithm.
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Extended and Unscented Kalman Filters for mobile robot localization and environment reconstruction
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In this work we compare the performance of two algorithms, respectively based on the Extended Kalman Filter and the Unscented Kalman Filter, for the mobile robot localization and environment reconstruction problem. The proposed algorithms do not require any assumption on the robot working space: they are driven only by the measurements taken using ultrasonic sensors located onboard the robot. We also devise a switching sensors activation policy, which allows energy saving still achieving accurate tracking and reliable mapping of the workspace. The results show that the two filters work comparably well, in spite of the superior theoretical properties of the Unscented Filter.
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In this article, we introduce a localization system to reduce the accumulation of errors existing in the dead-reckoning method of mobile robot localization. Dead-reckoning depends on the information that comes from the encoders. Many factors, such as wheel slippage, surface roughness, and mechanical tolerances, affect the accuracy of dead-reckoning. Therefore, an accumulation of errors exists in the dead-reckoning method. In this article, we propose a new localization system to enhance the localization operation of the mobile robots. The proposed localization system uses the extended Kalman filter combined with infrared sensors in order to solve the problems of dead-reckoning. The proposed system executes the extended Kalman filter cycle, using the walls in the working environment as references (landmarks), to correct errors in the robot's position (positional uncertainty). The accuracy and robustness of the proposed method are evaluated in the experiment results' section.
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Motion control of mobile robots and efficient trajectory tracking is usually based on prior estimation of the robots' state vector. To this end Gaussian and nonparametric filters (state estimators from position measurements) have been developed. In this paper the Extended Kalman Filter which assumes Gaussian measurement noise is compared to the Particle Filter which does not make any assumption on the measurement noise distribution. As a case study the estimation of the state vector of a mobile robot is used, when measurements are available from both odometric and sonar sensors. It is shown that in this kind of sensor fusion problem the Particle Filter has better performance than the Extended Kalman Filter, at the cost of more demanding computations.
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Most real systems are non-linear. Extended Kalman Filter (EKF) uses non-linear models of both the process and observation models while the Kalman Filter (KF) uses linear models. EKF is a good way to learn about Simultaneous Localisation and Mapping (SLAM). Much of the literature concentrates on advanced SLAM methods which stems from EKF or uses probabilistic techniques. This makes it difficult for new researchers to understand the basics of this exciting area of research. SLAM asks if it is possible for a robot, starting with no prior information, to move through its environment and build a consistent map of the entire environment. Additionally, the vehicle must be able to use the map to navigate and hence plan and control its trajectory during the mapping process. The applications of a robot capable of navigating, with no prior map, are diverse indeed. Domains in which 'man in the loop' systems are impractical or difficult such as sub-sea surveys and disaster zones are obvi...
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Applications involving wheeled mobile robots have been growing significantly in recent years thanks to its ability to move freely through space work, limited only by obstacles. Moreover, the wheels allow for greater convenience of transportation in environments plans and give greater support to the static robot. In the context of autonomous navigation of robots we highlight the localization problem. From an accumulated knowledge about the environment and using the current readings of the sensors, the robot must be able to determine and keep up its position and orientation in relation to this environment, even if the sensors have errors and / or noise. In other words, to localize a robot is necessary to determine its pose (position and orientation) in the workspace at a given time. Borenstein et al. (1997) have classified the localization methods in two great categories: relative localization methods, which give the robot’s pose relative to the initial one, and absolute localization ...
A Floating-point Extended Kalman Filter Implementation for Autonomous Mobile Robots
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Localization and Mapping are two of the most important capabilities for autonomous mobile robots and have been receiving considerable attention from the scientific computing community over the last 10 years. One of the most efficient methods to address these problems is based on the use of the Extended Kalman Filter (EKF). The EKF simultaneously estimates a model of the environment (map) and the position of the robot based on odometric and exteroceptive sensor information. As this algorithm demands a considerable amount of computation, it is usually executed on high end PCs coupled to the robot. In this work we present an FPGA-based architecture for the EKF algorithm that is capable of processing two-dimensional maps containing up to 1.8k features at real time (14Hz), a threefold improvement over a Pentium M 1.6GHz, and a 13-fold improvement over an ARM920T 200MHz. The proposed architecture also consumes only 1.3% of the Pentium and 12.3% of the ARM energy per feature.