An enhanced error model for EKF-based tightly-coupled integration of GPS and land vehicle’s motion sensors

Reduced inertial sensor systems (RISS) have been introduced by many researchers as a low-cost, low-complexity sensor assembly that can be integrated with GPS to provide a robust integrated navigation system for land vehicles. In earlier works, the developed error models were simplified based on the assumption that the vehicle is mostly moving on a flat horizontal plane. Another limitation is the simplified estimation of the horizontal tilt angles, which is based on simple averaging of the accelerometers’ measurements without modelling their errors or tilt angle errors. In this paper, a new error model is developed for RISS that accounts for the effect of tilt angle errors and the accelerometer’s errors. Additionally, it also includes important terms in the system dynamic error model, which were ignored during the linearization process in earlier works. An augmented extended Kalman filter (EKF) is designed to incorporate tilt angle errors and transversal accelerometer errors. The new error model and the augmented EKF design are developed in a tightly-coupled RISS/GPS integrated navigation system. The proposed system was tested on real trajectories’ data under degraded GPS environments, and the results were compared to earlier works on RISS/GPS systems. The findings demonstrated that the proposed enhanced system introduced significant improvements in navigational performance

Comparison between RGB and RGB-D cameras for supporting low-cost GNSS urban navigation

A pure GNSS navigation is often unreliable in urban areas because of the presence of obstructions, thus preventing a correct reception of the satellite signal. The bridging between GNSS outages, as well as the vehicle attitude reconstruction, can be recovered by using complementary information, such as visual data acquired by RGB-D or RGB cameras. In this work, the possibility of integrating low-cost GNSS and visual data by means of an extended Kalman filter has been investigated. The focus is on the comparison between the use of RGB-D or RGB cameras. In particular, a Microsoft Kinect device (second generation) and a mirrorless Canon EOS M RGB camera have been compared. The former is an interesting RGB-D camera because of its low-cost, easiness of use and raw data accessibility. The latter has been selected for the high-quality of the acquired images and for the possibility of mounting fixed focal length lenses with a lower weight and cost with respect to a reflex camera. The designed extended Kalman filter takes as input the GNSS-only trajectory and the relative orientation between subsequent pairs of images. Depending on the visual data acquisition system, the filter is different because RGB-D cameras acquire both RGB and depth data, allowing to solve the scale problem, which is instead typical of image-only solutions. The two systems and filtering approaches were assessed by ad-hoc experimental tests, showing that the use of a Kinect device for supporting a u-blox low-cost receiver led to a trajectory with a decimeter accuracy, that is 15% better than the one obtained when using the Canon EOS M camera

Impact of Indoor Location Information Reliability on Users’ Trust of an Indoor Positioning System

Indoor positioning systems have been used as a supplement to provide positioning in settings where GPS does not function. However, the accuracy of calculated results varies among techniques and algorithms used; system performance also differs across testing environments. As a result, users’ responses to and opinions of these positioning results could be different. Furthermore, user trust, most closely associated with their confidence in the system, will also vary. A relatively little studied topic is the effect of positioning variance on a user’s opinion or trust of such systems (GPS as well, for that matter). Therefore, understanding how user interaction with such systems (through trust) changes is important for achieving more usable positioning system design. An experiment was designed to examine if the sequence of location accuracy will affect users’ trust in an individual episode positioning result as well as the system overall. The simulated positioning system running on an iPad used for this experiment provides 10 priming positioning results at a specific category of accuracy. The accuracy is controlled and is presented as either 1. ACCURATE (within 5 meters of actual location), 2. INACCURATE (greater 15 meters), 0r 3. WRONG BUILDING (outside current building’s footprint). After one set of these priming locations a series of 55 post-priming locations across the same categories in addition to 10 CONTINUOUS locations (with between 6 and 15 meters of error) were presented. At each experimental site participants located themselves using the simulated system and rated their trust for that location. Variables obtained from the experiment include: 1. Two types of trust at each location (positioning trust and system trust); 2. Spatial abilities, sense of direction, and ancillary survey data (user characteristics). Results show that users’ trust varies among different accuracy categories and changes over time according to the system performance in association with their own characteristics. Specifically, the accuracy of the priming locations has an impact on users’ trust of later results. Besides, users’ trust in individual positioning results is quite variable and the variability is closely related to accuracy, while user trust of the overall system is less variable

Positioning Based on Tightly Coupled Multiple Sensors: A Practical Implementation and Experimental Assessment

During the last decade, the number of applications for land transportation that depend on systems for accurate positioning has significantly increased. Unfortunately, systems based on low-cost global navigation satellite system (GNSS) components harshly suffer signal impairments due to the environment surrounding the antenna, but new designs based on deeper data fusion and on the combination of different signal processing techniques can overcome limitations without the introduction of expensive components. Supported by a complete mathematical model, this paper presents the design of a real-time positioning system that is based on the tight integration of extremely low-cost sensors and a consumer-grade global positioning system receiver. The design has been validated experimentally through a series of tests carried out in real scenarios. The performance of the new system is compared against a standalone GNSS receiver and survey-grade professional equipment. The results show that a carefully designed and constrained integration of low-cost sensors can have performance comparable to that of an expensive professional equipment

A Study on Underwater Navigation System of Sensor Model-based Underwater Track Robot

In this study, an underwater navigation algorithm was developed to apply the underwater navigation system to the underwater track robot. Generally, underwater navigation uses a Doppler Velocity Log(DVL) to measure the velocity of underwater vehicles. However, undersea platforms, such as underwater track robots, cannot use DVL due to the distance limitations of sensor operation. As a result, Dead Reckoning(DR) navigation is inevitably used, and which results in severe errors in attitude and position values over long periods of platform operation. To overcome this problem, we developed an underwater navigation system composed of coupled Inertial Navigation System(INS) composed Ultra Short Base Line(USBL) and additional track information. The INS sensors were modeled using the mathematical model of the accelerometer, the gyroscope, the magnetometer. Before the experiment, computer simulations were performed to analyze the expected sensor values for specific track missions in unexpected situations. Based on this, we developed an underwater navigation algorithm for a prototype underwater track robot which we developed at the lab and confirmed the effectiveness of the navigation algorithm through experiments. For the prototype underwater track robot, we developed the navigation system, the electric hardware, the control system, and the operating system. Finally, we applied the developed INS and the underwater navigation algorithm to the platform and verified a good performance through real sea experiments.1. 서 론 1.1 연구 배경 1 1.1.1 수중트랙로봇 2 1.1.2 항법 5 1.2 연구 목표 7 1.3 논문 구성 8 2. 항법 센서의 수학적 모델 2.1 관성 측정 장치 9 2.1.1 가속도계 10 2.1.2 각속도계 15 2.1.3 자력계 20 3. 융합 항법 알고리즘 3.1 좌표계 25 3.2 INS 항법 알고리즘 설계 26 3.2.1 자세 추정 26 3.2.2 속도 추정 33 3.2.3 위치 추정 34 3.2.4 INS 오차 모델 35 3.3 융합 항법 알고리즘 설계 38 3.3.1 시스템 오차 모델 38 3.3.2 측정 오차 모델 41 3.4 융합 항법 알고리즘 구성 41 3.5 융합 항법 시뮬레이션 42 3.5.1 S자 궤적 항법 시뮬레이션 44 3.5.2 사각형 궤적 항법 시뮬레이션 50 3.5.3 모의 진회수 항법 시뮬레이션 56 4. 수중항법 시스템 및 플랫폼 구성 4.1 수중항법 시스템 63 4.1.1 시스템 구성 63 4.1.2 운용 시스템 67 4.2 수중트랙로봇 69 4.2.1 플랫폼 구성 69 4.2.2 운용 시스템 76 5. 융합 항법 성능 실험 5.1 트랜칭 수중트랙로봇에 적용한 융합 항법 성능 실험 77 5.1.1 Heading 추정 성능 실험 79 5.1.2 실해역 융합 항법 성능 실험 80 5.2 수중트랙로봇에 적용한 융합 항법 성능 실험 86 5.2.1 기본 항법 성능 실험 88 5.2.2 미션 궤적 항법 성능 실험 91 5.2.3 사각형 궤적 항법 성능 실험 #1 95 5.2.4 사각형 궤적 항법 성능 실험 #2 98 6. 결론 103 부록 106 참고문헌 114 감사의 글 119Docto

SenSys: A Smartphone-Based Framework for ITS applications

Intelligent transportation systems (ITS) use different methods to collect and process traffic data. Conventional techniques suffer from different challenges, like the high installation and maintenance cost, connectivity and communication problems, and the limited set of data. The recent massive spread of smartphones among drivers encouraged the ITS community to use them to solve ITS challenges. Using smartphones in ITS is gaining an increasing interest among researchers and developers. Typically, the set of sensors that comes with smartphones is utilized to develop tools and services in order to enhance safety and driving experience. GPS, cameras, Bluetooth, inertial sensors and other embedded sensors are used to detect and analyze drivers\u27 behavior and vehicles\u27 motion. The use of smartphones made the data collection process easier because of their availability among drivers, the set of different sensors, the computation ability, and the low installation and maintenance cost. On the other hand, different smartphones sensors have diverse characteristics and accuracy and each one of them needs special fusion, processing, and filtration methods to generate more stable and accurate data. Using smartphones in ITS faces different challenges like inaccurate readings, weak GPS reception, noisy sensors and unaligned readings.These challenges waste researchers and developers time and effort, and they prevent them from building accurate ITS applications. This work proposes SenSys a smartphone framework that collects and processes traffic data and then analyzes and extracts vehicle dynamics and vehicle activities which can be used by developers and researchers to create their navigation, communication, and safety ITS applications. SenSys framework fuses and filters smartphone\u27s sensors readings which result in enhancing the accuracy of tracking and analyzing various vehicle dynamics such as vehicle\u27s stops, lane changes, turn detection, and accurate vehicle speed calculation that, in turn, will enable development of new ITS applications and services