Cooperative localization by dual foot-mounted inertial sensors and inter-agent ranging

The implementation challenges of cooperative localization by dual foot-mounted inertial sensors and inter-agent ranging are discussed and work on the subject is reviewed. System architecture and sensor fusion are identified as key challenges. A partially decentralized system architecture based on step-wise inertial navigation and step-wise dead reckoning is presented. This architecture is argued to reduce the computational cost and required communication bandwidth by around two orders of magnitude while only giving negligible information loss in comparison with a naive centralized implementation. This makes a joint global state estimation feasible for up to a platoon-sized group of agents. Furthermore, robust and low-cost sensor fusion for the considered setup, based on state space transformation and marginalization, is presented. The transformation and marginalization are used to give the necessary flexibility for presented sampling based updates for the inter-agent ranging and ranging free fusion of the two feet of an individual agent. Finally, characteristics of the suggested implementation are demonstrated with simulations and a real-time system implementation.Comment: 14 page

Novel Environmental Features for Robust Multisensor Navigation

Many navigation techniques have now become so reliant on GNSS that there is no back up when there is limited or no signal reception. If there is interference, intentional or otherwise, with the signal, navigation could be lost or become misleading [1]. Other navigation techniques harness different technologies such as Wi-Fi [2], eLoran and inertial navigation. However, each of these techniques has its own limitations, such as coverage, degradation in urban areas or solution drift [3]. Therefore there is a need for new navigation and positioning techniques that may be integrated with GNSS to increase the reliability of the system as a whole. This paper presents the results of a feasibility study to identify a set of novel environmental features that could be used for navigation in the temporary absence of GNSS or degradation of the signal. By measuring these features during times of GNSS availability a map can be produced. This can be referred to during times of limited reception, a principle already used for some Wi-Fi positioning techniques [2]. Therefore a “measurable” can be defined as a feature either man-made or natural that is spatially distinct and has limited temporal variation. Possibilities considered include magnetic anomalies [4], light intensity and road signs. Firstly, a brainstorming exercise and a literature study were conducted to generate a list of possible environmental features that was assessed for the viability of each candidate. The features were ranked according to three criteria: practicality, precision and coverage. The definition of practicality for each measurable was that a suitable detector must be installable on a road vehicle, particularly an emergency vehicle, at a reasonable cost with minimal alterations to the vehicle. Precision was defined in terms of the spatial variation of the environmental feature and thus the accuracy with which position information might be derived from it. Coverage was assessed in terms of the availability of the feature over a range of different environments. Continuous coverage is not required because the new measurables may be used in combination and integrated with dead reckoning techniques, such as odometry and inertial navigation [3]. The outcome of the viability study was used to determine which features are to be experimentally tested. Magnetic anomalies, road texture and a dozen other environmental features were found to be worth investigation. Features which were discounted include wind speed and pulsars [5]. The initial experiment was carried out on foot in Central London. The same tests were repeated on two separate days, with a closed loop circuit walked three times on each occasion. This experiment used an Inertial Measurement Unit (IMU), comprising accelerometer and gyro triads, together with a barometer, three-axis magnetometer and GNSS receiver. The experiment was also recorded using a camcorder from the point of view of a pedestrian, enabling visual and audio features of the environment to be assessed. Magnetic anomalies were found to be a promising source of position information. Peaks in the magnetometer data were observed on all rounds at approximately the same positions. There were also similarities seen in the temperature profiles after correcting for the temporal variation of the background temperature. Another potential source of position information was found to be text-based signs. It is relatively simple to extract text from camera images and it is easily stored in a feature database. However, methods of dealing with identically-worded signs in close proximity will need to be developed. Sound levels were analysed in 10s intervals for the mean, minimum and maximum sound volume. There was no clear correlation observed between the different rounds of the experiment. Due to the pedestrian experimental results sound levels of the surroundings will not be used in further experimentation. An alternative area of enquiry for using sound (in the vehicular experiments) is using microphones to indirectly measure road texture based on the noise from the wheel contact with the road [6]. The paper will also present results of road vehicle experiments. Multiple circuits of the same routes will be compared. Different environments will be assessed including rural, dual carriageways, suburban and urban roads. Sensors to be used include the IMU and 3-axis magnetometer from the pedestrian experiment, a barometer, gas sensors, a microphone, an axle-mounted accelerometer, an ambient light sensor and a thermometer. These will be placed either on, inside or under the vehicle as determined by the individual needs of the sensors. The results will be used to determine which of these sensors could be potentially used for a multisensor integrated navigation system and also the environments in which they work optimally. Using the results of the three feasibility study phases (literature review, pedestrian and road experiment) the next project stage will be to produce a demonstration system that uses the most feasible features of the environment and creates a map database during times GNSS is present. This database will then be used for navigation in times of need. In the long term, it is envisaged that this technique will be implemented cooperatively, with a batch of vehicles collecting feature data and contributing it to a common shared database. / References [1] Thomas, M., et al., Global Navigation Space Systems: Reliance and Vulnerabilities, London, UK: Royal Academy of Engineering, 2011. [2] Jones, K., L. Liu, and F. Alizadeh-Shabdiz, “Improving Wireless Positioning with Look-ahead Map-Matching,” Proc. MobiQuitous 2007, Phildaelphia, PA, February 2008, pp. 1-8. [3] Groves, P.D., Principles of GNSS, Inertial, and Multisensor Intergrated Navigation Systems, Second Edition, Artech House, 2013. [4] Judd, T., and T. Vu, “Use of a New Pedometric Dead Reckoning Module in GPS Denied Environments,” Proc. IEEE/ION PLANS, Monterey, CA, May 2008, pp. 120?128. [5] Walter, D. J., "Feasibility study of novel environmental feature mapping to bridge GNSS outage," Young Navigator Conference, London, 2012. [6] Mircea, M., et al., “Strategic mapping of the ambient noise produced by road traffic, accordingly to European regulations,” Proc. IEEE International Conference on Automation, Quality and Testing, Robotics, Cluj Napoca, Romania, May 2008

An Adaptive Human Activity-Aided Hand-Held Smartphone-Based Pedestrian Dead Reckoning Positioning System

Pedestrian dead reckoning (PDR), enabled by smartphones’ embedded inertial sensors, is widely applied as a type of indoor positioning system (IPS). However, traditional PDR faces two challenges to improve its accuracy: lack of robustness for different PDR-related human activities and positioning error accumulation over elapsed time. To cope with these issues, we propose a novel adaptive human activity-aided PDR (HAA-PDR) IPS that consists of two main parts, human activity recognition (HAR) and PDR optimization. (1) For HAR, eight different locomotion-related activities are divided into two classes: steady-heading activities (ascending/descending stairs, stationary, normal walking, stationary stepping, and lateral walking) and non-steady-heading activities (door opening and turning). A hierarchical combination of a support vector machine (SVM) and decision tree (DT) is used to recognize steady-heading activities. An autoencoder-based deep neural network (DNN) and a heading range-based method to recognize door opening and turning, respectively. The overall HAR accuracy is over 98.44%. (2) For optimization methods, a process automatically sets the parameters of the PDR differently for different activities to enhance step counting and step length estimation. Furthermore, a method of trajectory optimization mitigates PDR error accumulation utilizing the non-steady-heading activities. We divided the trajectory into small segments and reconstructed it after targeted optimization of each segment. Our method does not use any a priori knowledge of the building layout, plan, or map. Finally, the mean positioning error of our HAA-PDR in a multilevel building is 1.79 m, which is a significant improvement in accuracy compared with a baseline state-of-the-art PDR system

Indoor location based services challenges, requirements and usability of current solutions

Indoor Location Based Services (LBS), such as indoor navigation and tracking, still have to deal with both technical and non-technical challenges. For this reason, they have not yet found a prominent position in people’s everyday lives. Reliability and availability of indoor positioning technologies, the availability of up-to-date indoor maps, and privacy concerns associated with location data are some of the biggest challenges to their development. If these challenges were solved, or at least minimized, there would be more penetration into the user market. This paper studies the requirements of LBS applications, through a survey conducted by the authors, identifies the current challenges of indoor LBS, and reviews the available solutions that address the most important challenge, that of providing seamless indoor/outdoor positioning. The paper also looks at the potential of emerging solutions and the technologies that may help to handle this challenge

Data-Driven Meets Navigation: Concepts, Models, and Experimental Validation

The purpose of navigation is to determine the position, velocity, and orientation of manned and autonomous platforms, humans, and animals. Obtaining accurate navigation commonly requires fusion between several sensors, such as inertial sensors and global navigation satellite systems, in a model-based, nonlinear estimation framework. Recently, data-driven approaches applied in various fields show state-of-the-art performance, compared to model-based methods. In this paper we review multidisciplinary, data-driven based navigation algorithms developed and experimentally proven at the Autonomous Navigation and Sensor Fusion Lab (ANSFL) including algorithms suitable for human and animal applications, varied autonomous platforms, and multi-purpose navigation and fusion approachesComment: 22 pages, 13 figure

A Review of pedestrian indoor positioning systems for mass market applications

In the last decade, the interest in Indoor Location Based Services (ILBS) has increased stimulating the development of Indoor Positioning Systems (IPS). In particular, ILBS look for positioning systems that can be applied anywhere in the world for millions of users, that is, there is a need for developing IPS for mass market applications. Those systems must provide accurate position estimations with minimum infrastructure cost and easy scalability to different environments. This survey overviews the current state of the art of IPSs and classifies them in terms of the infrastructure and methodology employed. Finally, each group is reviewed analysing its advantages and disadvantages and its applicability to mass market applications

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