Inertial Navigation and Position Uncertainty during a Blind Safe Stop of an Autonomous Vehicle

This work considers the problem of position and position-uncertainty estimation for atonomous vehicles during power black-out, where it cannot be assumed that any position data is accessible. To tackle this problem, the position estimation will instead be performed using power separated and independent measurement devices, including one inertial 6 Degrees of Freedom (DOF) measurement unit, four angular wheel speed sensors and one pinion angle sensor. The measurement unit\u27s sensors are initially characterized in order to understand conceptual limitations of the inertial navigation and also to be used in a filtering process. Measurement models are then fused together with vehicle dynamics process models using the architecture of an Extended Kalman Filter (EKF). Two different EKF filter concepts are developed to estimate the vehicle position during a safe stop; one simpler filter for smooth manoeuvres and a complex filter for aggressive manoeuvres. Both filter designs are tested and evaluated with data gathered from an experimental vehicle for selected manoeuvres of developed safe-stop scenarios. The experimental results from a set of use-case manoeuvres show a trend where the size of the position estimation errors significantly grows above an initial vehicle speed of 70 km/h. This paper contributes to develop vehicle dynamics models for the purpose of a blind safe stop

Three-dimensional millimetre wave beam tracking based on handset MEMS sensors with extended Kalman filtering

Due to the narrow beam in millimetre wave communication for future 5G networks, small device movements in the form of either self-rotation or displacement can result in serious power loss. In this paper, a three-dimensional (3-D) beam tracking method employing extended Kalman filtering (EKF) is proposed based on antenna arrays and the three smart phone sensors, which are gyroscope, accelerometer and magnetometer, embedded in the micro-electro-mechanical system (MEMS) inside the smart phone. The EKF-based location tracking is also incorporated into the design by combing the data from direction of arrival (DoA) and time of arrival (ToA) estimation results of the user node (UN), since accurate UN location information is also very important in the process of beam tracking to achieve beam alignment between the access node (AN) and the UN

Multisensor navigation systems: a remedy for GNSS vulnerabilities?

Space-based positioning, navigation, and timing (PNT) technologies, such as the global navigation satellite systems (GNSS) provide position, velocity, and timing information to an unlimited number of users around the world. In recent years, PNT information has become increasingly critical to the security, safety, and prosperity of the World's population, and is now widely recognized as an essential element of the global information infrastructure. Due to its vulnerabilities and line-of-sight requirements, GNSS alone is unable to provide PNT with the required levels of integrity, accuracy, continuity, and reliability. A multisensor navigation approach offers an effective augmentation in GNSS-challenged environments that holds a promise of delivering robust and resilient PNT. Traditionally, sensors such as inertial measurement units (IMUs), barometers, magnetometers, odometers, and digital compasses, have been used. However, recent trends have largely focused on image-based, terrain-based and collaborative navigation to recover the user location. This paper offers a review of the technological advances that have taken place in PNT over the last two decades, and discusses various hybridizations of multisensory systems, building upon the fundamental GNSS/IMU integration. The most important conclusion of this study is that in order to meet the challenging goals of delivering continuous, accurate and robust PNT to the ever-growing numbers of users, the hybridization of a suite of different PNT solutions is required

Three-dimensional millimetre-wave beam tracking based on smart phone sensor measurements and direction of arrival/time of arrival estimation for 5G networks

The narrow millimetre-wave beam in future 5G networks is easily interrupted by the movement of mobile handsets including both location change and self-rotation. In this study, a three-dimensional beam tracking method is proposed to achieve beam alignment between the access node and the user node (UN). A gradient descent algorithm is employed for self-rotation tracking based on measurements obtained by the three smart phone sensors (gyroscope, accelerometer and magnetometer) embedded in the micro-electro-mechanical system. An extended Kalman filtering -based location tracking algorithm is also incorporated into the design by combining the data from the direction of arrival and time of arrival estimation results of the UN since accurate UN location information is also crucial in the beam tracking process. Moreover, an operation protocol is developed to coordinate the tracking process and tested in different scenarios

Inertial Measurement Unit based Virtual Antenna Arrays - DoA Estimation and Positioning in Wireless Networks

Today we have different location based services available in a mobile phone or mobile station (MS). These services include: direction finding to nearby ATMs, locating favorite food restaurants, or finding any target destination. Similarly, we see different applications of the positioning and navigation systems in firefighting or other rescue operations. The common factor in almost all of the location based services is the system's ability to determine the user's current position, with reference to a floor plan or a navigation map. Current technologies are using sensor data measurements from one or more sensors, available to the positioning device, for positioning and navigation. Typical examples are radio based positioning such as global positioning system, inertial sensors based inertial navigation system, or camera based positioning systems. Different accuracy and availability conditions of the positioning and navigation solution can be obtained depending on the positioning algorithms and the available sensor information.Nowadays, the focus of research in positioning and navigation has been mostly on the use of existing hardware infrastructure and low-cost solutions, such that the proposed technique can be deployed with ease and without extra infrastructure requirements as well as without any expensive sensor equipment. In this work, we investigate a novel idea for positioning using existing wireless networks and low-cost inertial sensor measurements available at the MS. We propose to use received baseband radio signal along with inertial sensor data, such as accelerometer and rate gyroscope measurements, for direction of arrival (DoA) estimation and positioning. The DoA information from different base stations or access points can be used to estimate the MS position using triangulation technique. Furthermore, due to size and cost restrictions it is difficult to have real antenna arrays at the MS, the idea of DoA estimation and positioning is proposed to be used with single antenna devices by using the so-called virtual antenna arrays.We have presented our research results in three different papers. We provide measurement based results to perform a quantitative evaluation of DoA estimation using arbitrary virtual antenna arrays in 3-D; where a state-of-the-art high-resolution algorithm has been used for radio signal parameter estimation. Furthermore, we provide an extended Kalman filter framework to investigate the performance of unaided inertial navigation systems with 3-axis accelerometer and 3-axis rate gyroscope measurements, from a six-degrees-of-freedom inertial measurement unit. Using the extended Kalman filter framework, we provide results for position estimation error standard deviation with respect to integration time for an unaided inertial navigation system; where the effect of different stochastic errors noise sources in the inertial sensors measurements such as white Gaussian noise and bias instability noise is investigated. Also, we derive a closed form expression for Cramér-Rao lower bound to investigate DoA estimation accuracy for a far-field source using random antenna arrays in 3-D. The Cramér-Rao lower bound is obtained using known antenna coordinates as well as using estimated antenna coordinates, where the antenna coordinates are estimated with an uncertainty whose standard deviation is known. Furthermore, using Monte-Carlo simulations for random antenna arrays, we provide Cramér-Rao lower bound based performance evaluation of random 3-D antenna arrays for DoA estimation

Towards IMU-based Full-body Motion Estimation of Rough Terrain Mobile Manipulators

For navigation or pose estimation, strap-down Micro-Electro-Mechanical System (MEMS) Inertial Measurement Units (IMU) are widely used in all types of mobile devices and applications, from mobile phones to cars and heavy-duty Mobile Working Machines (MWM). This thesis is a summary of work focus on the utilization of IMUs for state estimation of MWM. Inertial sensor-based technology offers an alternative to the traditional solution, since it can significantly decrease the system cost and improve its robustness. For covering the research topic of whole-body estimation with IMUs, five publications focus on the development of novel algorithms, which use sensor fusion or rotary IMU theory to estimate or calculate the states of MWM. The test-platforms are also described in detail. First, we used low-cost IMUs installed on the surface of a hydraulic arm to estimate the joint state. These robotic arms are installed on a floating base, and the joints of the arms rotate in a two-dimensional (2D) plane. The novel algorithm uses an Extended Kalman Filter (EKF) to fuse the output of the gyroscopes and the accelerometers, with gravity as the reference. Second, a rotary gyroscope is mounted on a grasper of a crane, and the rotary gyroscope theory is implemented to decrease the drift of the angular velocity measurement. Third, low-cost IMUs are attached to the wheels and the bogie test bed, and the realization of IMU-based wheel odometry is investigated. Additionally, the rotary gyroscope provides information about the roll and yaw attitude for the test bed. Finally, we used an industry grade IMU fuse with the output of wheel odometry to estimate the position and attitude of the base for an MWM moving on slippery ground. One of the main aims of this research study is to estimate the states of an MWM only using IMU sensors. The research achievements indicate this approach is promising. However, the observability of IMU in the yaw direction of the navigation frame is limited so it is difficult to estimate the yaw angle of the rotation plane for the robotic arm when only using IMUs, to ensure the long-term reliable yaw angle and position of the vehicle base, external information might also be needed. When applying the rotary IMU theory, minimization of the power supply for the rotation device is still a challenge. This research study demonstrates that IMUs can be low-cost and reliable replacements for traditional sensors in joint angle measurement and in the wheel rotation angle for vehicles, among other applications. An IMU can also provide a robust state for a vehicle base in a challenging environment. These achievements will benefit future developments of MWMs in remote control and autonomous operations

Off-line evaluation of indoor positioning systems in different scenarios: the experiences from IPIN 2020 competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001

Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements

Automotive applications of high precision GNSS

This thesis aims to show that Global Navigation Satellite Systems (GNSS) positioning can play a significant role in the positioning systems of future automotive applications. This is through the adoption of state-of-the-art GNSS positioning technology and techniques, and the exploitation of the rapidly developing vehicle-to-vehicle concept. The merging together of these two developments creates greater performance than can be achieved separately. The original contribution of this thesis comes from this combination: Through the introduction of the Pseudo-VRS concept. Pseudo-VRS uses the princples of Network Real Time Kinematic (N-RTK) positioning to share GNSS information between vehicles, which enables absolute vehicle positioning. Pseudo-VRS is shown to improve the performance of high precision GNSS positioning for road vehicles, through the increased availability of GNSS correction messages and the rapid resolution of the N-RTK fixed solution. Positioning systems in the automotive sector are dominated by satellite-based solutions provided by GNSS. This has been the case since May 2001, when the United States Department of Defense switched off Selective Availability, enabling significantly improved positioning performance for civilian users. The average person most frequently encounters GNSS when using electronic personal navigation devices. The Sat Nav or GPS Navigator is ubiquitous in modern societies, where versions can be found on nomadic devices such as smartphones and dedicated personal navigation devices, or built in to the dashboards of vehicles. Such devices have been hugely successful due to their intrinsic ability to provide position information anywhere in the world with an accuracy of approximately 10 metres, which has proved ideal for general navigation applications. There are a few well known limitations of GNSS positioning, including anecdotal evidence of incorrect navigation advice for personal navigation devices, but these are minor compared to the overall positioning performance. Through steady development of GNSS positioning devices, including the integration of other low cost sensors (for instance, wheel speed or odometer sensors in vehicles), and the development of robust map matching algorithms, the performance of these devices for navigation applications is truly incredible. However, when tested for advanced automotive applications, the performance of GNSS positioning devices is found to be inadequate. In particular, in the most advanced fields of research such as autonomous vehicle technology, GNSS positioning devices are relegated to a secondary role, or often not used at all. They are replaced by terrestrial sensors that provide greater situational awareness, such as radar and lidar. This is due to the high performance demand of such applications, including high positioning accuracy (sub-decimetre), high availability and continuity of solutions (100%), and high integrity of the position information. Low-cost GNSS receivers generally do not meet such requirements. This could be considered an enormous oversight, as modern GNSS positioning technology and techniques have significantly improved satellite-based positioning performance. Other non-GNSS techniques also have their limitations that GNSS devices can minimise or eliminate. For instance, systems that rely on situational awareness require accurate digital maps of their surroundings as a reference. GNSS positioning can help to gather this data, provide an input, and act as a fail-safe in the event of digital map errors. It is apparent that in order to deliver advanced automotive applications - such as semi- or fully-autonomous vehicles - there must be an element of absolute positioning capability. Positioning systems will work alongside situational awareness systems to enable the autonomous vehicles to navigate through the real world. A strong candidate for the positioning system is GNSS positioning. This thesis builds on work already started by researchers at the University of Nottingham, to show that N-RTK positioning is one such technique. N-RTK can provide sub-decimetre accuracy absolute positioning solutions, with high availability, continuity, and integrity. A key component of N-RTK is the availability of real-time GNSS correction data. This is typically delivered to the GNSS receiver via mobile internet (for a roving receiver). This can be a significant limitation, as it relies on the performance of the mobile communications network, which can suffer from performance degradation during dynamic operation. Mobile communications systems are expected to improve significantly over the next few years, as consumers demand faster download speeds and wider availability. Mobile communications coverage already covers a high percentage of the population, but this does not translate into a high percentage of a country's geography. Pockets of poor coverage, often referred to as notspots, are widespread. Many of these notspots include the transportation infrastructure. The vehicle-to-vehicle concept has made significant forward steps in the last few years. Traditionally promoted as a key component of future automotive safety applications, it is now driven primarily by increased demand for in-vehicle infotainment. The concept, which shares similarities with the Internet of Things and Mobile Ad-hoc Networks, relies on communication between road vehicles and other road agents (such as pedestrians and road infrastructure). N-RTK positioning can take advantage of this communication link to minimise its own communications-related limitations. Sharing GNSS information between local GNSS receivers enables better performance of GNSS positioning, based on the principles of differential GNSS and N-RTK positioning techniques. This advanced concept is introduced and tested in this thesis. The Pseudo VRS concept follows the protocols and format of sharing GNSS data used in N-RTK positioning. The technique utilises the latest GNSS receiver design, including multiple frequency measurements and high quality antennas

Review and classification of vision-based localisation techniques in unknown environments

International audienceThis study presents a review of the state-of-the-art and a novel classification of current vision-based localisation techniques in unknown environments. Indeed, because of progresses made in computer vision, it is now possible to consider vision-based systems as promising navigation means that can complement traditional navigation sensors like global navigation satellite systems (GNSSs) and inertial navigation systems. This study aims to review techniques employing a camera as a localisation sensor, provide a classification of techniques and introduce schemes that exploit the use of video information within a multi-sensor system. In fact, a general model is needed to better compare existing techniques in order to decide which approach is appropriate and which are the innovation axes. In addition, existing classifications only consider techniques based on vision as a standalone tool and do not consider video as a sensor among others. The focus is addressed to scenarios where no a priori knowledge of the environment is provided. In fact, these scenarios are the most challenging since the system has to cope with objects as they appear in the scene without any prior information about their expected position