Map Matching Methods Research Articles

Mobile Robot Navigation Utilizing the WEB Based Aerial Images Without Prior Teaching Run

In order to realize the work of goods conveyance etc. by robot, localization of robot position is fundamental technology component. Map matching methods is one of the localization technique. In map matching method, usually, to create the map data for localization, we have to operate the robot and measure the environment (teaching run). This operation requires a lot of time and work. In recent years, due to improved Internet services, aerial image data is easily obtained from Google Maps etc. Therefore, we utilize the aerial images as a map data to for mobile robots localization and navigation without teaching run. In this paper, we proposed the robot localization and navigation technique using aerial images. We verified the proposed technique by the localization and autonomous running experiment.

A Novel Semantic Matching Method for Indoor Trajectory Tracking

The rapid development of smartphone sensors has provided rich indoor pedestrian trajectory data for indoor location-based applications. To improve the quality of these collected trajectory data, map matching methods are widely used to correct trajectories. However, these existing matching methods usually cannot achieve satisfactory accuracy and efficiency and have difficulty in exploiting the rich information contained in the obtained trajectory data. In this study, we proposed a novel semantic matching method for indoor pedestrian trajectory tracking. Similar to our previous work, pedestrian dead reckoning (PDR) and human activity recognition (HAR) are used to obtain the raw user trajectory data and the corresponding semantic information involved in the trajectory, respectively. To improve the accuracy and efficiency for user trajectory tracking, a semantic-rich indoor link-node model is then constructed based on the input floor plan, in which navigation-related semantics are extracted and formalized for the following trajectory matching. PDR and HAR are further utilized to segment the trajectory and infer the semantics (e.g., “Turn left”, “Turn right”, and “Go straight”). Finally, the inferred semantic information is matched with the semantic-rich indoor link-node model to derive the correct user trajectory. To accelerate the matching process, the semantics inferred from the trajectory are also assigned weights according to their relative importance. The experiments confirm that the proposed method achieves accurate trajectory tracking results while guaranteeing a high matching efficiency. In addition, the resulting semantic information has great application potential in further indoor location-based services.

Post-processing GPS-tracks in reconstructing travelled routes in a GIS-environment: network subset selection and attribute adjustment

ABSTRACTThe exact distance and routes travelled on an individual level are essential variables in determining the effectiveness of ‘soft’ transport demand management strategies. The ability to track individuals in great spatial detail by means of Location Aware Technologies such as GPS has opened avenues for gathering these data with great precision. Route reconstruction with positional data is typically done by a process referred to as map-matching; however, despite the large number of real-time map-matching algorithms developed, few studies have developed a post-processing map-matching algorithm in a Geographic Information Systems (GIS)-environment. This paper presents two GIS-based map-matching methods that predominantly use a digital road network with speed and directionality attributes for route reconstruction of raw GPS-trajectories. The methodologies were tested for a data set in which actual routes travelled were known. Both explored procedures, the ‘Connected Subset’ assignment procedure based on network subset selection and the ‘Impedance Reduction’ assignment procedure based on attribute adjustment, provide accurate results. In addition, both procedures effectively deal with commonly GPS-induced problems such as measurement gaps and positional drift.

A ST-CRF Map-Matching Method for Low-Frequency Floating Car Data

Integrating raw Global Position System (GPS) trajectories with a road network is often referred to as a map-matching problem. However, low-frequency trajectories (e.g., one GPS point for every 1–2 min) have raised many challenges to existing map-matching methods. In this paper, we propose a novel and global spatial–temporal map-matching method called spatial and temporal conditional random field (ST-CRF), which is based on insights relating to: 1) the spatial positioning accuracy of GPS points with the topological information of the underlying road network; 2) the spatial–temporal accessibility of a floating car; 3) the spatial distribution of the middle point between two consecutive GPS points; and 4) the consistency of the driving direction of a GPS trajectory. We construct a conditional random field model and identify the best matching path sequence from all candidate points. A series of experiments conducted for real environments using mass floating car data collected in Beijing and Shanghai shows that the ST-CRF method not only has better performance and robustness than other popular methods (e.g., point-line, ST-matching, and interactive voting-based map-matching methods) in low-frequency map matching but also solves the “label-bias” problem, which has long existed in the map matching of classical hidden Markov-based methods.

Curvedness feature constrained map matching for low-frequency probe vehicle data

ABSTRACTMap matching method is a fundamental preprocessing technique for massive probe vehicle data. Various transportation applications need map matching methods to provide highly accurate and stable results. However, most current map matching approaches employ elementary geometric or topological measures, which may not be sufficient to encode the characteristic of realistic driving paths, leading to inefficiency and inaccuracy, especially in complex road networks. To address these issues, this article presents a novel map matching method, based on the measure of curvedness of Global Positioning System (GPS) trajectories. The curvature integral, which measures the curvedness feature of GPS trajectories, is considered to be one of the major matching characteristics that constrain pairwise matching between the two adjacent GPS track points. In this article, we propose the definition of the curvature integral in the context of map matching, and develop a novel accurate map matching algorithm based on the curvedness feature. Using real-world probe vehicles data, we show that the curvedness feature (CURF) constrained map matching method outperforms two classical methods for accuracy and stability under complicated road environments.

Improvement Schemes for Indoor Mobile Location Estimation: A Survey

Location estimation is significant in mobile and ubiquitous computing systems. The complexity and smaller scale of the indoor environment impose a great impact on location estimation. The key of location estimation lies in the representation and fusion of uncertain information from multiple sources. The improvement of location estimation is a complicated and comprehensive issue. A lot of research has been done to address this issue. However, existing research typically focuses on certain aspects of the problem and specific methods. This paper reviews mainstream schemes on improving indoor location estimation from multiple levels and perspectives by combining existing works and our own working experiences. Initially, we analyze the error sources of common indoor localization techniques and provide a multilayered conceptual framework of improvement schemes for location estimation. This is followed by a discussion of probabilistic methods for location estimation, including Bayes filters, Kalman filters, extended Kalman filters, sigma-point Kalman filters, particle filters, and hidden Markov models. Then, we investigate the hybrid localization methods, including multimodal fingerprinting, triangulation fusing multiple measurements, combination of wireless positioning with pedestrian dead reckoning (PDR), and cooperative localization. Next, we focus on the location determination approaches that fuse spatial contexts, namely, map matching, landmark fusion, and spatial model-aided methods. Finally, we present the directions for future research.

A probabilistic map matching method for smartphone GPS data

Smartphones have the capability of recording various kinds of data from built-in sensors such as GPS in a non-intrusive, systematic way. In transportation studies, such as route choice modeling, the discrete sequences of GPS data need to be associated with the transportation network to generate meaningful paths. The poor quality of GPS data collected from smartphones precludes the use of state of the art map matching methods. In this paper, we propose a probabilistic map matching approach. It generates a set of potential true paths, and associates a likelihood with each of them. Both spatial (GPS coordinates) and temporal information (speed and time) is used to calculate the likelihood of the data for a specific path. Applications and analyses on real trips illustrate the robustness and effectiveness of the proposed approach. Also, as an application example, a Path-Size Logit model is estimated based on a sample of real observations. The estimation results show the viability of applying the proposed method in a real route choice modeling context.

New Map-matching Method Based on Decision Circle for GNSS-based ETC Systems

AbstractA decision circle is proposed for map-matching in the complex region of a road network, and a new map-matching algorithm is introduced to resolve the inaccuracy in GNSS-based ETC or road navigation systems. The decision circle can be set in particular regions and particular map-matching methods can be adopted. Accurate positioning points on road links are collected beforehand instead of creating map links to enable point-to-point map-matching, and not point-to-link, such as in traditional map-matching. By computing the relations between GPS positioning points and accurate points, the relation between GPS positioning points and links is decided, two steps are applied to determine the link where vehicles are located.

A pedestrian-oriented map matching algorithm for map information sharing systems in disaster areas

In this paper, we propose a pedestrian-oriented map matching algorithm for tracking moving trajectories of pedestrians. Our algorithm is designed for a map information sharing system among refugees in disaster areas. In this situation, refugees have mobile terminals with GPS devices and move to shelters at walking speed. Thus, our algorithm has to be suitable for battery-driven mobile terminals. In order to reduce battery consumption, our algorithm is based on a geometric curve-to-curve matching approach in which computation resources are less required in comparison with other complicated approaches such as probabilistic map matching, statistical map matching and so on. Furthermore, in order to deal with matching errors, our algorithm has following features: initial matching point selection, candidate road segments selection and incremental matching method. We conduct experiments with real time sequence data captured by GPS. Experimental results shows that our proposed algorithm can achieve the best result in comparison with the other conventional geometric map matching methods.

An Integrated Map-Match Algorithm with Position Feedback and Shape-Based Mismatch Detection and Correction

Global Positioning System (GPS)-based vehicle navigation systems have been rapidly developed over recent years. In urban areas, due to the problems of satellite signal blockage and severe multipath effects, GPS may not be available for a long period of time. Integrated GPS/Dead Reckoning (DR) systems are commonly used to bridge GPS position gaps. Moreover, map-matching (MM) techniques can be implemented to improve positioning accuracy by locating an estimated vehicle position (from GPS/DR) on road; however, the reliability of map-matching methods is still a problem because vehicle position may be located to an incorrect road section (mismatch) due to large vehicle positioning errors from GPS/DR and complexity of road network, which is a typical environment in urban areas. In this article, we present a tightly integrated vehicle navigation system to overcome these problems. Performance of any MM algorithms is closely linked with position data quality. In our approach, we integrate MM results with GPS/DR output at the sensor level for data quality control and optimal estimation of sensor errors and vehicle position. Thus quality of position input to MM unit is significantly improved. Furthermore, GPS/DR output, together with road network, is used to detect and to automatically correct any mismatches occurred. We have implemented this integrated approach to a prototype vehicle navigation system and extensive tests have been carried out in Hong Kong urban areas. Testing results demonstrate that the performance of the new system is significantly improved in terms of accuracy, coverage, and reliability.