LIRO: Tightly Coupled Lidar-Inertia-Ranging Odometry

One of the problems faced by soccer robots is how to find out the position of the robot itself and other robots on the field. A simple way to find out the robot's position is to use the odometry method. However, odometry is weak in accumulating position errors that reduce the accuracy of the moving robot's absolute position estimation and orientation. This paper presents a robot position data estimation system that is to be implemented on the ERSOW wheeled soccer robot. The robot can determine its position based on a unique landmark: an L-shaped line on the soccer robot field. We use a deep neural network method to recognize landmark L-shaped. Vision systems and deep learning inferences run on the Robot Operating System platform. After obtaining the distance of the robot to the L-shaped landmark, the robot's orientation and position relative to the field can be accurately determined based on the omnidirectional camera's perception. The results of the position estimation system in this study can be used to reduce position errors resulting from the odometry method. Based on the landmark L-shape recognition test results conducted on 641 datasets, the validation accuracy value is 0.806. The results of testing the robot position generated by vision obtained the largest error x about 2.32 cm and y about 1.99 cm.

As a fundamental prerequisite for a variety of location-based services, indoor location information has received increasing attention in recent years. Under the line-of-sight condition, the positioning accuracy of the indoor positioning technology based on ultrawideband (UWB) is acceptable for many applications, but under the non-line-of-sight condition, it degrades dramatically. The positioning accuracy can be significantly improved by the fusion of inertial measurement units and UWB sensors based on the extended Kalman filter (EKF) algorithm. However, when UWB measurements are affected by large non-Gaussian noise, the assumption of the EKF algorithm that observations are subject to Gaussian distribution for noise is invalid. Although the non-Gaussian noise can be handled by the robust EKF algorithm, this algorithm only uses the prior information to judge the reliability of the observations, and the positioning result is not stable when the number of beacons is small. To solve this problem, a method for successive updating of the covariance and posterior state of the observations in iterations based on an iterated extended Kalman filter (IEKF) is proposed. The marginal distribution of the posterior distribution is constructed and iteratively optimized, inhibiting the effect of non-Gaussian noise on UWB under a complex environment. The positioning results of the proposed method, the standard EKF algorithm, and the robust EKF algorithm, using different numbers of beacons, are compared. The results show that the positioning accuracy of the proposed algorithm is the highest under all scenarios. The proposed algorithm shows the smallest decrease in accuracy and presents the most stable positioning when the number of beacons is small, which is a common situation in practical applications.

Since there are many interferences in the indoor environment, it is difficult to achieve the precise positioning of the mobile robot using a single sensor. This paper presents a position estimation and positioning error correction method of mobile robots based on multisensor data. The robot’s positioning sensor includes ultra-wideband (UWB) components, inertial measurement unit (IMU), and encoders. UWB multipath interference causes more ranging errors, which can be reduced by the correction equation after data fitting. The real-time coordinates of the UWB robot tag can be calculated based on multiple UWB anchor data and the least squares method. The coordinate data x c , y c are acquired by UWB positioning subsystem, and the velocity data x ̇ c , y ̇ c are collected by IMU together with encoders. The multisensor data continuously update Kalman filter and estimate robot position. In the positioning process, the positioning data of different sensors can be mutually corrected and supplemented. The results of UWB ranging correction experiments indicate that data fitting can improve the UWB positioning accuracy. In the multisensor positioning experiments, compared with a single sensor, the positioning method based on data fusion of UWB, IMU, and encoders has higher accuracy and adaptability. When UWB signals are interfered or invalid, other sensors can still work normally and complete the robot positioning process. The multisensor positioning method not only improves the robot positioning accuracy but also has stronger environmental adaptability.

This paper considers the global localization of a pedestrian via an ultra-wideband (UWB) ranging aided inertial navigation system (INS) and aims to address the challenges involved in proper processing of UWB range measurements. Even though UWB offers a decimeter level accuracy in line-of-sight (LoS) ranging, its accuracy degrades significantly in non-line-of-sight (NLoS). This drop in accuracy is due to a significant unknown positive bias in the NLOS range measurements. Therefore, the measurement models used in UWB LoS and NLoS ranging conditions are different, and proper processing of NLoS measurements requires a bias compensation measure. Previous work on bias compensation for UWB ranging that is used to aid an INS based localization assumes that the LoS and NLoS measurements are identified and distinguished from each other with absolute certainty. However, in practice, this assumption is hard to satisfy, and identifiers that determine the type of UWB range measurements deliver their results with only some level of certainty. To take into account the probabilistic nature of the NLoS identifiers, in this paper, we propose an adaptive localization based on the first-order generalized pseudo Bayesian (GPB) method to seamlessly handle the measurement model switching between LoS and NLoS UWB range measurements. The effectiveness of our proposed method is demonstrated via an experiment for pedestrian geolocation using a shoe-mounted INS system aided by UWB range measurements with respect to beacons with known locations.

In this paper, we present the results of the measurements and modeling of the propagation channel for ultra wide band (UWB) communications in large indoor environments. The selected environment is a sports center of large dimensions ($51 \times 26$ m, 7.4m height) without intermediate obstacles so that there is always direct path (DP) between TX and RX and UWB technology can be used for critical communications of sensors. On this environment, narrow band and ultra wide band measurements have been made with a high resolution pulse channel sounder (8ns) at the nominal frequency of 3.9 GHz. Narrow band measurements have been used to model path loss for the DP, while for UWB measurements, the power delay profile have been used to obtain the statistics of the multipath of the channel. Combining both models has been possible to obtain a detailed characterization of the environment. The results constitute a complete characterization of this type of indoor environments for UWB signals and can be easily extrapolated to other environments with similar characteristics, allowing the use of UWB technology for applications of critical communications for sensors and other devices.

A compact 4 × 4 reconfigurable MIMO antenna design with adjustable suppression of certain frequency bands within the UWB frequency range

In this article, a resilient tightly coupled ultra-wideband (UWB) visual–inertial indoor localization system (R-UVIS) is developed to obtain accurate and robust localization performance in complex scenes, even in the case when sensors fail. More specifically, three schemes are designed for the proposed system. First, we introduce the line and image patch features to improve the precision and robustness of the visual features. Besides, we propose accurate loop closure and relocalization methods based on multifeatures to improve the performance of the localization system. Second, we introduce the UWB sensor into the system to suppress the localization drifts in the complex scenes and provide a fixed reference frame. Third, we propose a resilient multisensor fusion method based on an optimization framework to fuse the UWB, visual and inertial measurements in a tightly coupled manner. This data fusion approach improves the robustness of the system, making the localization system seamlessly switch among different localization modes depending on the specific scenes. In addition, an initialization process along with three sensors is also designed for the whole system. We conduct extensive experiments on the public dataset and in real-world scenarios to evaluate the proposed R-UVIS. The experimental results show that the proposed R-UVIS can provide the accurate and robust localization results in a fixed coordinate system for the complicated indoor scenes, even in the case when visual tracking fails or the UWB anchors are unavailable.

The objective of this research is to develop a real time obstacle detection and obstacle avoidance for autonomous navigation of mobile robots using a stereo camera in an unstructured environment. Autonomous navigation of mobile robots demands a) Exact determination of position and orientation of Robot and b) Accurate determination of size, shape, depth and range of potential obstacles in the environment. Simple kinematic model is used for mobile robot and stereo camera with pan and tilt provision is considered for long range operation. Complete 3D reconstruction of object/obstacle is obtained from the stereo matching algorithm and with triangulation method. The pose (position and orientation) of mobile robot is formulated from the static object observation with stereo reference matching points using RANSAC (RANdom SAmple Consensus) in successive frames. Potential field based obstacle avoidance formulation is carried out by using the obstacle range, size information, mobile robot position and orientation. Finally, proportional derivative navigation control loop along with obstacle avoidance algorithm is formulated and verified.

In this paper we consider a Simultaneous Localization and Mapping (SLAM) problem for a moving agent using Visual Odometry (VO) while measuring the range from a set of Ultra Wide Band (UWB) antennas, deployed in unknown position in the environment. The solution approach is based on a switching observer which, under standard working conditions, for each observed UWB, uses a two dimensional Extended Kalman Filter (EKF) providing an estimate of the range and bearing of the observed UWB with respect to the agent. This information is then used in a Robust EKF algorithm which solves the SLAM problem with performances that, even before closing the loop, are comparable to the ones that a VO algorithm (namely ORB-SLAM2) would obtain only after closing the loop. Moreover, a resilient module is added to the algorithm to evaluate the reliability of the position estimate of each observed UWB. When the Visual Odometry is not available, the switching observer uses an auxiliary EKF to provide an estimate of the agent position. This makes the proposed approach robust with respect to several kinds of unmodeled disturbances, like multipath effects, and automatically adapts to sensor failures with resilience (e.g. when Visual Odometry or UWB measurements are not available).

<span>A frequency reconfigurable antenna is presented in this paper as a novel single port system gathering the functionalities of both underlay and interweave cognitive radio. This 25×30×0.8 mm<sup>3</sup> system involves a wide slot antenna to cover the ultra wide band (UWB) range with resonating stubs that are used to prohibit/work-only-in specific bands within the UWB frequencies. Here, six positive intrinsic negative (PIN) diodes are used to decide the active sections of the antenna that leads to select its operation as UWB/filtering/multiband antenna. Diodes' configuration results in eight useful operation modes that include a scanning mode, four single band-notch modes and three dual band communication modes. The scanning mode covers the entire UWB range while one of the bands allocated for WiMax, Cband, WLAN or Xband is to be excluded in each of the band-notch modes. On the other hand, each communaication mode is able to work in one of the ranges that cover WiMax/Cband, Cband/WLAN or Xband/international telecommunication union (ITU). S11, realized gain, voltage standing wave ratio (VSWR) outcomes of this design that is simulated by computer simulation technology (CST) v.10 all confirms the proposed system's ability to work in the intended modes. Its novelity to work as interweave/underlay cognitive radio system, highly candidates this design to address many of the UWB communication issues related interference and multiband operation</span>

In this research paper, design & simulation of a super wide band microstrip patch antenna, a ultrawideband (UWB) microstrip filter and a UWB microstrip filtenna using HFSS vs 15 software have been proposed. Firstly, a rectangular shape monopole patch antenna and a parallel coupled microstrip UWB bandpass filter (BPF) are separately designed. The proposed antenna is designed on a low cost FR4 substrate with height of 1.6 mm and loss tangent of 0.02. The antenna is then integrated with UWB bandpass filter. The integrated antenna filter system is called UWB filtering antenna or filtenna. Simulated results in the terms of peak gain and bandwidth for the proposed antenna are obtained 5.9 dBi and 16.9 GHz in the frequency range 3.1 to 20 GHz, respectively. It is seen that the BPF shows the simulated transmission characteristics in the entire UWB band in better extend. The simulated result of the proposed filtenna exhibits better frequency response in UWB range as well as better out of band rejection capability. Compact size and bandwidth of the proposed UWB microstrip filtenna which exhibits better UWB band characteristics are obtained as 15 × 27 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 7.46 GHz in the frequency range 3.1 to 10.6 GHz, respectively.

Indoor positioning is usually based on individual technologies that provide estimates of the trajectory of the person, or measures the ranges or angles between the user and known positions. Each technique has its advantages and problems, and a common way to overcome the drawbacks of single-technology solutions is to fuse the information from several system, but due to their non linear measurements, there is no optimal linear solution. We propose the use of a particle filter to fuse foot mounted inertial measurements with any additional Radio Frequency (RF) measurement. The information fusion is achieved propagating the position of the particles using the relative step displacements obtained from foot mounted Pedestrian Dead Reckoning (PDR), and updating the weights of the particles according to the RF measurements. In our experiments the inertial unit was located in the foot of the user, and the RF system consisted in a Radio Frequency Identification (RFID) receiver in the waist, and an Ultra Wide band (UWB) tag in the chest, but the scheme can be used in any sensor configuration. As the UWB measurements have a significant amount of outliers due to non line of sight conditions generated by the position of the tag in the body, received reflections, etc., we propose a new outlier rejection algorithm based on the compatibility of groups of measurements. The fusion was tested evaluating the inclusion of each of the RF systems and varying the number RFID tags used. The proposed method is able to locate a person with less than 2 m of error (for 90 % of the obtained estimations) in the studied trajectory. This particle filter scheme offers robust indoor positioning with 100 % availability and smooth trajectory estimation thanks to the PDR and limited error due to the RF measurements.

Combining ultra-wideband (UWB) and low-cost inertial measurement unit (IMU) by a tight coupling fashion is an effective way to provide an integrated indoor localization system with high accuracy and continuity. However, the performance of such an integrated system may be poor in the presence of undesirable geometry between an UWB target node and a number of UWB anchors. In addition, some parameters in the state vector of Kalman filter (used for data fusion) are unobservable from UWB ranges. These problems have not been discussed in detail in the literature related to UWB localization. In this paper, we analyze the performance of integrated localization under different geometries between the UWB node and anchors based on Fisher information matrix. Also, the parameter observability with respect to UWB ranges is analyzed.

Achieving accurate, reliable and globally consistent localization is a fundamental challenge for autonomous mobile robots. There have been numerous researches in this topic showing impressive performance in various scenarios and with various sensor combinations. In a multi-robot setting, finding the relative positions between robots is another essential issue that inhibits other high level tasks such as obstacle avoidance, path planning and collaborative tasking. These problems are especially critical for Micro Aerial Vehicles (MAVs) since any collisions with the surrounding objects or neighbor robots can be catastrophic. Furthermore, the size, weight and power constraints are much more restrictive for MAVs as opposed to ground or legged robots. In this regard, Ultra-wideband (UWB) provides many advantages compared to traditional sensors such as camera, radar or LiDAR. However, UWB has its own limitations such as being heavily reliant on line-of-sight (LoS) and being unable to provide any information about the environment. For these reasons, designing new methods that can combine UWB and other sensor modalities is a worthwhile endeavour and also the overarching goal of this thesis. Firstly, we investigate the problem of integrating UWB and a monocular camera, specifically fusing ranging and visual odometry (VO) data to estimate both the UWB anchor position and the VO's scale factor. We propose a loosely-coupled (LC) fusion scheme to address this problem, with the main advantage being its flexibility since both the VO algorithm and UWB sensor can be replaced easily. Additionally, a theoretical analysis is provided, including the derivation of Fisher Information Matrix (FIM), the FIM's determinant, an evaluation of the singular configurations and their geometric interpretations. Based on these analyses, an estimation-trajectory optimization framework is presented which consists of an estimator, which is designed based on gradient-descent or semidefinite programming (SDP) optimization, and a trajectory optimization approach that minimizes the uncertainty volume and can be easily adapted to different scenarios. Nonetheless, the VO's estimate will still drift over time as the error accumulates which cannot be addressed by the LC method. Hence, we continue to develop a tightly-coupled (TC) version that fuses both UWB and visual measurements in a joint optimization pipeline, supports any number of anchors and incorporates various other enhancements. Extensive simulation and experiments are carried out to demonstrate the effectiveness of the proposed LC and TC systems. Overall, our LC method is fast but susceptible to long-term drift while our TC method is more accurate but more computationally expensive, and the estimation results can be further improved thanks to the trajectory optimization method, even in challenging conditions. Having established the pros and cons for both LC and TC systems, the TC variant is chosen for further researches as we focus on the system performance. In our next works, we extend the fusion scheme to that of UWB and visual-inertial odometry (VIO). Existing methods discard most of the UWB data and ignore the spatial-temporal offsets between sensors. Our approach, on the other hand, addresses these issues by leveraging the propagated information readily available from the VIO pipeline and generalize the UWB processing model. As a result, our method uses the UWB data more effectively: the time-offset between measurements is compensated and all available measurements can be used. Experimental results show that our system consistently surpass previous works in localizing the anchor position and reducing the long-term odometry drift. Lastly, we explore UWB-aided methods for two important problems in multi-robot systems: 1) onboard localization, i.e. finding the position of each robot in a global frame, 2) relative localization, i.e. finding the relative position and heading between robots. Through the derivation of the FIM and its determinant, an analysis for unobservable configurations and their physical interpretation are provided. We then propose optimization-based methods to estimate the relative frame transformation accurately even with no initial guesses. These estimates are then continuously refined based on the robot's motion model and online data, while the onboard sensor data are tightly fused to correct the drift. Our method works without loop closure while requiring less exchanged data, which makes it both flexible and resource-efficient. Moreover, practical issues ignored in previous works are addressed, such as rejecting inter-robot UWB ranging outliers and checking for singular configurations. Simulations and experiments verify that our system as a whole or as its individual parts outperform previous approaches, and the theoretical results are in line with real-world observations.

The implementation of a common control channel is one of the most challenging issues in cognitive radio networks, since a fully reliable control channel cannot be created without reserving bandwidth specifically for this purpose. In this paper, we investigate a promising solution that exploits the Ultra Wide Band (UWB) technology to let cognitive radio nodes discover each other and exchange control information for establishing a communication link. The contribution of this paper is threefold: (i) we define the communication protocol needed to let cognitive radio nodes discover each other and exchange control information for link set up, (ii) we overcome the gap in coverage, which typically exists between UWB and long-medium range technologies, by using multihop communications, (iii) we evaluate the performance of our approach and show its feasibility through extensive simulations.