Accurate pedestrian indoor navigation by tightly coupling foot-mounted IMU and RFID measurements

We present a new method to accurately locate persons indoors by fusing inertial navigation system (INS) techniques with active RFID technology. A foot-mounted inertial measuring units (IMUs)-based position estimation method, is aided by the received signal strengths (RSSs) obtained from several active RFID tags placed at known locations in a building. In contrast to other authors that integrate IMUs and RSS with a loose Kalman filter (KF)-based coupling (by using the residuals of inertial- and RSS-calculated positions), we present a tight KF-based INS/RFID integration, using the residuals between the INS-predicted reader-to-tag ranges and the ranges derived from a generic RSS path-loss model. Our approach also includes other drift reduction methods such as zero velocity updates (ZUPTs) at foot stance detections, zero angular-rate updates (ZARUs) when the user is motionless, and heading corrections using magnetometers. A complementary extended Kalman filter (EKF), throughout its 15-element error state vector, compensates the position, velocity and attitude errors of the INS solution, as well as IMU biases. This methodology is valid for any kind of motion (forward, lateral or backward walk, at different speeds), and does not require an offline calibration for the user gait. The integrated INS+RFID methodology eliminates the typical drift of IMU-alone solutions (approximately 1% of the total traveled distance), resulting in typical positioning errors along the walking path (no matter its length) of approximately 1.5 m.

Localization system for pedestrians based on sensor and information fusion

Nowadays there is an increase of location-aware mobile applications. However, these applications only retrieve location with a mobile device's GPS chip. This means that in indoor or in more dense environments these applications don't work properly. To provide location information everywhere a pedestrian Inertial Navigation System (INS) is typically used, but these systems can have a large estimation error since, in order to turn the system wearable, they use low-cost and low-power sensors. In this work a pedestrian INS is proposed, where force sensors were included to combine with the accelerometer data in order to have a better detection of the stance phase of the human gait cycle, which leads to improvements in location estimation. Besides sensor fusion an information fusion architecture is proposed, based on the information from GPS and several inertial units placed on the pedestrian body, that will be used to learn the pedestrian gait behavior to correct, in real-time, the inertial sensors errors, thus improving location estimation.

Mathematical Model and Simulation of a Pneumatic Apparatus for In-Drilling Alignment of an Inertial Navigation Unit during Horizontal Well Drilling

Conventional methods in horizontal drilling processes incorporate magnetic surveying techniques for determining the position and orientation of the bottom-hole assembly (BHA). Such means result in an increased weight of the drilling assembly, higher cost due to the use of non-magnetic collars necessary for the shielding of the magnetometers, and significant errors in the position of the drilling bit. A fiber-optic gyroscope (FOG) based inertial navigation system (INS) has been proposed as an alternative to magnetometer -based downhole surveying. The utilizing of a tactical-grade FOG based surveying system in the harsh downhole environment has been shown to be theoretically feasible, yielding a significant BHA position error reduction (less than 100m over a 2-h experiment). To limit the growing errors of the INS, an in-drilling alignment (IDA) method for the INS has been proposed. This article aims at describing a simple, pneumatics-based design of the IDA apparatus and its implementation downhole. A mathematical model of the setup is developed and tested with Bloodshed Dev-C++. The simulations demonstrate a simple, low cost and feasible IDA apparatus.

A recommendation for designing mobile pedestrian navigation system in university campuses

University campuses have thousands of new students, staff and visitors every year. For those who are unfamiliar with the campus environment, an effective pedestrian navigation system is essential to orientate and guide them around the campus. Compared to traditional navigation systems, such as physical signposts and digital map kiosks, a mobile pedestrian navigation system provides advantages in terms of mobility, sensing capabilities, weather-awareness when the user is on the go. However, how best to design a mobile pedestrian navigation system for university campuses is still vague due to limited research in understanding how pedestrians interact with the system, and what information is required for traveling in a complex environment such as university campus. In this paper, we present a mobile pedestrian navigation system called QUT Nav. A field study with eight participants was run in a university campus context, aiming to identify key information required in a mobile pedestrian navigation system for user traveling in university campuses. It also investigated user's interactions and behaviours while they were navigating in the campus environment. Based on the results from the field study, a recommendation for designing mobile pedestrian navigation systems for university campuses is stated.

Tutorial: implementing a pedestrian tracker using inertial sensors

Shoe-mounted inertial sensors offer a convenient way to track pedestrians in situations where other localization systems fail. This tutorial outlines a simple yet effective approach for implementing a reasonably accurate tracker. This Web extra presents the Matlab implementation and a few sample recordings for implementing the pedestrian inertial tracking system using an error-state Kalman filter for zero-velocity updates (ZUPTs) and orientation estimation.

The Information-analytical System for Diagnostics of Aircraft Navigation Units

The operation of technical processes requires increasingly advanced supervision and fault diagnostics to improve reliability and safety. This paper gives an introduction to the field of fault detection and diagnostics and has short methods classification. Growth of complexity and functional importance of inertial navigation systems leads to high losses at the equipment refusals. The paper is devoted to the INS diagnostics system development, allowing identifying the cause of malfunction. The practical realization of this system concerns a software package, performing a set of multidimensional information analysis. The project consists of three parts: subsystem for analyzing, subsystem for data collection and universal interface for open architecture realization. For a diagnostics improving in small analyzing samples new approaches based on pattern recognition algorithms voting and taking into account correlations between target and input parameters will be applied. The system now is at the development stage.

Bearing-only SLAM : a vision-based navigation system for autonomous robots

To navigate successfully in a previously unexplored environment, a mobile robot must be able to estimate the spatial relationships of the objects of interest accurately. A Simultaneous Localization and Mapping (SLAM) sys- tem employs its sensors to build incrementally a map of its surroundings and to localize itself in the map simultaneously. The aim of this research project is to develop a SLAM system suitable for self propelled household lawnmowers. The proposed bearing-only SLAM system requires only an omnidirec- tional camera and some inexpensive landmarks. The main advantage of an omnidirectional camera is the panoramic view of all the landmarks in the scene. Placing landmarks in a lawn field to define the working domain is much easier and more flexible than installing the perimeter wire required by existing autonomous lawnmowers. The common approach of existing bearing-only SLAM methods relies on a motion model for predicting the robot’s pose and a sensor model for updating the pose. In the motion model, the error on the estimates of object positions is cumulated due mainly to the wheel slippage. Quantifying accu- rately the uncertainty of object positions is a fundamental requirement. In bearing-only SLAM, the Probability Density Function (PDF) of landmark position should be uniform along the observed bearing. Existing methods that approximate the PDF with a Gaussian estimation do not satisfy this uniformity requirement. This thesis introduces both geometric and proba- bilistic methods to address the above problems. The main novel contribu- tions of this thesis are: 1. A bearing-only SLAM method not requiring odometry. The proposed method relies solely on the sensor model (landmark bearings only) without relying on the motion model (odometry). The uncertainty of the estimated landmark positions depends on the vision error only, instead of the combination of both odometry and vision errors. 2. The transformation of the spatial uncertainty of objects. This thesis introduces a novel method for translating the spatial un- certainty of objects estimated from a moving frame attached to the robot into the global frame attached to the static landmarks in the environment. 3. The characterization of an improved PDF for representing landmark position in bearing-only SLAM. The proposed PDF is expressed in polar coordinates, and the marginal probability on range is constrained to be uniform. Compared to the PDF estimated from a mixture of Gaussians, the PDF developed here has far fewer parameters and can be easily adopted in a probabilistic framework, such as a particle filtering system. The main advantages of our proposed bearing-only SLAM system are its lower production cost and flexibility of use. The proposed system can be adopted in other domestic robots as well, such as vacuum cleaners or robotic toys when terrain is essentially 2D.

Hearing loss and a supportive tactile signal in a navigation system : effects on driving behavior and eye movements

An on-road study was conducted to evaluate a complementary tactile navigation signal on driving behaviour and eye movements for drivers with hearing loss (HL) compared to drivers with normal hearing (NH). 32 participants (16 HL and 16 NH) performed two preprogrammed navigation tasks. In one, participants received only visual information, while the other also included a vibration in the seat to guide them in the correct direction. SMI glasses were used for eye tracking, recording the point of gaze within the scene. Analysis was performed on predefined regions. A questionnaire examined participant's experience of the navigation systems. Hearing loss was associated with lower speed, higher satisfaction with the tactile signal and more glances in the rear view mirror. Additionally, tactile support led to less time spent viewing the navigation display.

Исследования и испытания алгоритмов автоматической пилотажно-навигационной системы малогабаритного беспилотного летательного аппарата = Research and testing of automatic navigation management system’s algorithms for small unmanned aerial vehicle

В статье представлено развитие принципа построения автоматической пилотажно-навигационной системы (АПНС) для беспилотного летательного аппарата (БЛА). Принцип заключается в синтезе комплексных систем управления БПЛА не только на основе использования алгоритмов БИНС, но и алгоритмов, объединяющих в себе решение задач формирования и отработки сформированной траектории резервированной системой управления и навигации. Приведены результаты аналитического исследования и данные летных экспериментов разработанных алгоритмов АПНС БЛА, обеспечивающих дополнительное резервирование алгоритмов навигации и наделяющих БЛА новым функциональной способностью по выходу в заданную точку пространства с заданной скоростью в заданный момент времени с учетом атмосферных ветровых возмущений. Предложена и испытана методика идентификации параметров воздушной атмосферы: направления и скорости W ветра. Данные летных испытаний полученного решения задачи терминальной навигации демонстрируют устойчивую работу синтезированных алгоритмов управления в различных метеоусловиях. The article presents a progress in principle of development of automatic navigation management system (ANMS) for small unmanned aerial vehicle (UAV). The principle defines a development of integrated control systems for UAV based on tight coupling of strap down inertial navigation system algorithms and algorithms of redundant flight management system to form and control flight trajectory. The results of the research and flight testing of the developed ANMS UAV algorithms are presented. The system demonstrates advanced functional redundancy of UAV guidance. The system enables new UAV capability to perform autonomous multidimensional navigation along waypoints with controlled speed and time of arrival taking into account wind. The paper describes the technique for real-time identification of atmosphere parameters such as wind direction and wind speed. The flight test results demonstrate robustness of the algorithms in diverse meteorological conditions.

Robot navigation using human cues: A robot navigation system for symbolic goal-directed exploration

In this paper we present for the first time a complete symbolic navigation system that performs goal-directed exploration to unfamiliar environments on a physical robot. We introduce a novel construct called the abstract map to link provided symbolic spatial information with observed symbolic information and actual places in the real world. Symbolic information is observed using a text recognition system that has been developed specifically for the application of reading door labels. In the study described in this paper, the robot was provided with a floor plan and a destination. The destination was specified by a room number, used both in the floor plan and on the door to the room. The robot autonomously navigated to the destination using its text recognition, abstract map, mapping, and path planning systems. The robot used the symbolic navigation system to determine an efficient path to the destination, and reached the goal in two different real-world environments. Simulation results show that the system reduces the time required to navigate to a goal when compared to random exploration.

Design and analysis of ARROW-based racetrack resonator vibration sensor for inertial navigation application

In this paper we propose and analyze a novel racetrack resonator based vibration sensor for inertial grade application. The resonator is formed with an Anti Resonance Reflecting Optical Waveguide (ARROW) structure which offers the advantage of low loss and single mode propagation. The waveguide is designed to operate at 1310nm and TM mode of propagation since the Photo-elastic co-efficient is larger than TE mode in a SiO2/ Si3N4/ SiO2. The longer side of the resonator is placed over a cantilever beam with a proof mass. A single bus waveguide is coupled to the resonator structure. When the beam vibrates the resonator arm at the foot of the cantilever experiences maximum stress. Due to opto-mechanical coupling the effective refractive index of the resonator changes hence the resonance wavelength shifts. The non uniform cantilever beam has a dimension of 1.75mm X 0.45mm X 0.020mm and the proof mass has a dimension of 3mm X 3mm X 0.380mm. The proof mass lowers the natural frequency of vibration to 410Hz, hence designed for inertial navigation application. The operating band of frequency is from DC to 100Hz and acceleration of less than 1g. The resonator has a Free Spectral Range (FSR) of 893pm and produces a phase change of 22.4mrad/g.

Fusing the Navigation Information of Dual Foot-Mounted Zero-Velocity-Update-Aided Inertial Navigation Systems

A range constraint method viz. centroid method is proposed to fuse the navigation information of dual (right and left) foot-mounted Zero-velocity-UPdaTe (ZUPT)-aided Inertial Navigation Systems (INSs). Here, the range constraint means that the distance of separation between the position estimates of right and left foot ZUPT-aided INSs cannot be greater than a quantity known as foot-to-foot maximum separation. We present the experimental results which illustrate the applicability of the proposed method. The results show that the proposed method significantly enhances the accuracy of the navigation solution when compared to using two uncoupled foot-mounted ZUPT-aided INSs. Also, we compare the performance of the proposed method with the existing data fusion methods.

SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-Oriented Navigation System

Both animals and mobile robots, or animats, need adaptive control systems to guide their movements through a novel environment. Such control systems need reactive mechanisms for exploration, and learned plans to efficiently reach goal objects once the environment is familiar. How reactive and planned behaviors interact together in real time, and arc released at the appropriate times, during autonomous navigation remains a major unsolved problern. This work presents an end-to-end model to address this problem, named SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-oriented Navigation system. The model comprises several interacting subsystems, governed by systems of nonlinear differential equations. As the animat explores the environment, a vision module processes visual inputs using networks that arc sensitive to visual form and motion. Targets processed within the visual form system arc categorized by real-time incremental learning. Simultaneously, visual target position is computed with respect to the animat's body. Estimates of target position activate a motor system to initiate approach movements toward the target. Motion cues from animat locomotion can elicit orienting head or camera movements to bring a never target into view. Approach and orienting movements arc alternately performed during animat navigation. Cumulative estimates of each movement, based on both visual and proprioceptive cues, arc stored within a motor working memory. Sensory cues are stored in a parallel sensory working memory. These working memories trigger learning of sensory and motor sequence chunks, which together control planned movements. Effective chunk combinations arc selectively enhanced via reinforcement learning when the animat is rewarded. The planning chunks effect a gradual transition from reactive to planned behavior. The model can read-out different motor sequences under different motivational states and learns more efficient paths to rewarded goals as exploration proceeds. Several volitional signals automatically gate the interactions between model subsystems at appropriate times. A 3-D visual simulation environment reproduces the animat's sensory experiences as it moves through a simplified spatial environment. The SOVEREIGN model exhibits robust goal-oriented learning of sequential motor behaviors. Its biomimctic structure explicates a number of brain processes which are involved in spatial navigation.

A Kinect Based Indoor Navigation System for the Blind

Team NAVIGATE aims to create a robust, portable navigational aid for the blind. Our prototype uses depth data from the Microsoft Kinect to perform realtime obstacle avoidance in unfamiliar indoor environments. The device augments the white cane by performing two signi cant functions: detecting overhanging objects and identifying stairs. Based on interviews with blind individuals, we found a combined audio and haptic feedback system best for communicating environmental information. Our prototype uses vibration motors to indicate the presence of an obstacle and an auditory command to alert the user to stairs ahead. Through multiple trials with sighted and blind participants, the device was successful in detecting overhanging objects and approaching stairs. The device increased user competency and adaptability across all trials.