Geographic Centroid Routing for Vehicular Networks

A number of geolocation-based Delay Tolerant Networking (DTN) routing protocols have been shown to perform well in selected simulation and mobility scenarios. However, the suitability of these mechanisms for vehicular networks utilizing widely-available inexpensive Global Positioning System (GPS) hardware has not been evaluated. We propose a novel geolocation-based routing primitive (Centroid Routing) that is resilient to the measurement errors commonly present in low-cost GPS devices. Using this notion of Centroids, we construct two novel routing protocols and evaluate their performance with respect to positional errors as well as traditional DTN routing metrics. We show that they outperform existing approaches by a significant margin.Comment: 6 page

Navigation capabilities of mid-cost GNSS/INS vs. smartphone analysis and comparison in urban navigation scenarios

Proceedings of: 17th International Conference on Information Fusion (FUSION 2014): Salamanca, Spain 7-10 July 2014.High accuracy navigation usually require expensive sensors and/or its careful integration into a complex and finely tuned system. Smartphones pack a high number of sensors in a portable format, becoming a source of low-quality information with a high heterogeneity and redundancy. This work compares pure GNSS/INS capabilities on both types of platform, and discuss the weaknesses/opportunities offered by the smartphone. The analysis is carried out in a modular context-aware sensor fusion architecture developed for a previous work. It intends to serve as a preparation for answering bigger questions: can smartphones provide robust and high-quality navigation in vehicles? In which conditions? Where are the limits in the different navigation scenarios?This work was supported in part by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485)Publicad

SILS: a Smart Indoors Localization Scheme based on on-the-go cooperative Smartphones networks using onboard Bluetooth, WiFi and GNSS

Seamless outdoors-indoors localization based on Smartphones sensors is essential to realize the full potential of Location Based Services. This paper proposes a Smart Indoors Localization Scheme (SILS) whereby participating Smartphones (SPs) in the same outdoors and indoors vicinity, form a Bluetooth network to locate the indoors SPs. To achieve this, SILS will perform 3 functions: (1) synchronize & locate all reachable WiFi Access Points (WAPs) with live GNSS time available on the outdoors SPs; 2) exchange a database of all SPs location and time-offsets; 3) calculate approximate location of indoor-SPs based on hybridization of GNSS, Bluetooth and WiFi measurements. These measurements includes a) Bluetooth to Bluetooth relative pseudo ranges of all participating SPs based on hop-synchronization and Master-Slave role switching to minimize the pseudo-ranges error, b) GNSS measured location of outdoors-SPs with good geometric reference points, and c) WAPs-SPs Trilateration estimates for deep indoors localization. Results, obtained from OPNET simulation and live trials of SILS built for various SPs network size and indoors/outdoors combinations scenarios, show that we can locate under 1 meter in near-indoors while accuracy of around 2-meters can be achieved when locating SPs at deep indoors situations. Better accuracy can be achieved when large numbers of SPs (up to 7) are available in the network/vicinity at any one time and when at least 4 of them have a good sky view outdoors

Intelligent GNSS Positioning using 3D Mapping and Context Detection for Better Accuracy in Dense Urban Environments

Conventional GNSS positioning in dense urban areas can exhibit errors of tens of meters due to blockage and reflection of signals by the surrounding buildings. Here, we present a full implementation of the intelligent urban positioning (IUP) 3D-mapping-aided (3DMA) GNSS concept. This combines conventional ranging-based GNSS positioning enhanced by 3D mapping with the GNSS shadow-matching technique. Shadow matching determines position by comparing the measured signal availability with that predicted over a grid of candidate positions using 3D mapping. Thus, IUP uses both pseudo-range and signal-to-noise measurements to determine position. All algorithms incorporate terrain-height aiding and use measurements from a single epoch in time. Two different 3DMA ranging algorithms are presented, one based on least-squares estimation and the other based on computing the likelihoods of a grid of candidate position hypotheses. The likelihood-based ranging algorithm uses the same candidate position hypotheses as shadow matching and makes different assumptions about which signals are direct line-of-sight (LOS) and non-line-of-sight (NLOS) at each candidate position. Two different methods for integrating likelihood-based 3DMA ranging with shadow matching are also compared. In the position-domain approach, separate ranging and shadow-matching position solutions are computed, then averaged using direction-dependent weighting. In the hypothesis-domain approach, the candidate position scores from the ranging and shadow matching algorithms are combined prior to extracting a joint position solution. Test data was recorded using a u-blox EVK M8T consumer-grade GNSS receiver and a HTC Nexus 9 tablet at 28 locations across two districts of London. The City of London is a traditional dense urban environment, while Canary Wharf is a modern environment. The Nexus 9 tablet data was recorded using the Android Nougat GNSS receiver interface and is representative of future smartphones. Best results were obtained using the likelihood-based 3DMA ranging algorithm and hypothesis-based integration with shadow matching. With the u-blox receiver, the single-epoch RMS horizontal (i.e., 2D) error across all sites was 4.0 m, compared to 28.2 m for conventional positioning, a factor of 7.1 improvement. Using the Nexus tablet, the intelligent urban positioning RMS error was 7.0 m, compared to 32.7 m for conventional GNSS positioning, a factor of 4.7 improvement. An analysis of processing and data requirements shows that intelligent urban positioning is practical to implement in real-time on a mobile device or a server. Navigation and positioning is inherently dependent on the context, which comprises both the operating environment and the behaviour of the host vehicle or user. No single technique is capable of providing reliable and accurate positioning in all contexts. In order to operate reliably across different contexts, a multi-sensor navigation system is required to detect its operating context and reconfigure the techniques accordingly. Specifically, 3DMA GNSS should be selected when the user is in a dense urban environment, not indoors or in an open environment. Algorithms for detecting indoor and outdoor context using GNSS measurements and a hidden Markov model are described and demonstrated

Outlier Detection for 3D-Mapping-Aided GNSS Positioning

This paper takes 3D-mapping-aided (3DMA) GNSS as an example and investigates the outlier detection for pattern matching based positioning. Three different test statistics, two in the measurement domain and one in the position domain, are presented. Two 3D city maps with different levels of detail were used, one of which contained two obvious errors, to demonstrate the performance of 3DMA GNSS positioning in the presence of errors in the mapping data. The experiments tested were conducted alongside busy roads in the London Borough of Camden, where a total of 8 sets of 2-minute static pedestrian navigation data were collected with a u-blox EVK M8T GNSS receiver. The results confirm that both 3D mapping errors and temporary environmental changes (such as passing vehicles) can have a significant negative impact on the performance of 3DMA GNSS positioning. After applying outlier detection, single-epoch 3DMA GNSS algorithm reduces the horizontal RMS position error by approximately 15% compared to that without outlier detection. The filtering algorithm attenuates the effects of temporary environmental changes, providing an improvement of about 15% over single-epoch positioning, while the outlier algorithm further reduces the RMS error to a comparable level to that of using high-accuracy maps, about 4.7m

Multi-Epoch 3D-Mapping-Aided Positioning using Bayesian Filtering Techniques

In dense urban areas, conventional GNSS does not perform satisfactorily, sometimes resulting in errors of tens of metres. This is due to the blocking, reflection and diffraction of GNSS satellite signals by obstructions such as buildings and moving vehicles. The 3D mapping data of buildings can be used to predict which GNSS signals are line-of-sight (LOS) and which are non-line-of-sight (NLOS). These data have been shown to greatly improve GNSS performance in urban environments. Location-based services typically use single-epoch positioning, while all pedestrian and vehicle navigation applications use filtered solutions. Filtering can reduce the impact of noise-like errors on the position solution. Kalman filtering-based solutions have been adopted as one of the standard algorithms for GNSS navigation in many different products, and particle filtering has been demonstrated by several research groups. This paper mainly investigates the performance of different filtering algorithms combined with 3D-mapping-aided (3DMA) techniques. In addition to the Kalman filter and particle filter, the grid filter is also considered. In contrast to a particle filter, the hypotheses of a grid filter are uniformly distributed (forming a grid), but with different likelihoods, which better fits the physics of the problem. At the same time, this allows the current UCL’s single-epoch 3DMA GNSS positioning algorithm to be easily extended to multi-epoch situations. This paper then compares the performance of these continuous positioning algorithms in urban environments. The datasets used for testing include pedestrian and vehicle navigation data, covering two main application scenarios that often appear in cities. Pedestrian navigation data is static, and was collected in the City of London using a u-blox EVK M8T GNSS receiver. The vehicle navigation data consists of GPS and Galileo measurements, collected in Canary Wharf by a trials van with a Racelogic Labsat 3 GNSS front end. Subsequently, these data are fed into several single- and multi-epoch filtering algorithms, including single-epoch conventional GNSS, single-epoch 3DMA GNSS, conventional extended Kalman Filter (EKF), conventional particle filter (PF), 3DMA GNSS particle filter (PF), and 3DMA GNSS grid filter (GF). The results show that filtering has a greater impact on the results of mobile positioning with significant movement compared to static positioning. In vehicle tests, the conventional multi-epoch GNSS algorithms improve positioning accuracy by more than 40% compared to single-epoch GNSS, whereas in static positioning they deliver a limited improvement. 3DMA GNSS significantly improves positioning accuracy in the denser environments, but provides little benefit in more open areas. The 3DMA GNSS techniques and the filtering algorithms benefit each other. The former provides the latter with a better position solution at the measurement update step, while the latter in turn repays the former with a better initial position and a smaller search area. In vehicle tests at Canary Wharf, the 3DMA GNSS filtering reduces the overall solution error by approximately 50% and 40% compared to the single-epoch 3DMA GNSS and filtered conventional GNSS, respectively. Thus, multi-epoch 3DMA GNSS filtering should bring maximum benefit to mobile positioning in dense environments. The results from both datasets also confirm that the performance of 3DMA GNSS particle filtering and grid filtering are similar in terms of positional accuracy. In terms of efficiency, 3DMA GNSS grid filtering uses fewer particles to achieve the same coverage of the search area as particle filtering