Localization of Non-Linearly Modeled Autonomous Mobile Robots Using Out-of-Sequence Measurements

This paper presents a state of the art of the estimation algorithms dealing with Out-of-Sequence (OOS) measurements for non-linearly modeled systems. The state of the art includes a critical analysis of the algorithm properties that takes into account the applicability of these techniques to autonomous mobile robot navigation based on the fusion of the measurements provided, delayed and OOS, by multiple sensors. Besides, it shows a representative example of the use of one of the most computationally efficient approaches in the localization module of the control software of a real robot (which has non-linear dynamics, and linear and non-linear sensors) and compares its performance against other approaches. The simulated results obtained with the selected OOS algorithm shows the computational requirements that each sensor of the robot imposes to it. The real experiments show how the inclusion of the selected OOS algorithm in the control software lets the robot successfully navigate in spite of receiving many OOS measurements. Finally, the comparison highlights that not only is the selected OOS algorithm among the best performing ones of the comparison, but it also has the lowest computational and memory cost

Micro-scale monitoring in an urban area using vehicular sensor network

One of the emerging problems facing populated urban areas represents high level of the air pollution. Current monitoring approaches assume processing of pollution data in a centralised manner, however due to external factors (e.g. current atmospheric conditions) the air pollution level can have fast fluctuations inside a street which might lead to disparity of the pollution levels in neighbouring streets. This thesis proposes a decentralised monitoring framework that enables harvesting of the pollution data, its processing, and dissemination of early warnings to the Static Monitoring Units (SMU) when the onsets of hazardous air pollution concentration are detected at the street (micro-scale) level. The proposed air pollution monitoring framework relies on a Vehicular Sensor Network infrastructure where vehicles have limited on-board resources, such as processing, battery power and storage. Three aspects of the micro-scale air pollution monitoring have been investigated. The Decentralised data Dissemination and Harvesting (DDH) is a single-hop dissemination mechanism which enables the nodes to decide whether they should harvest the data based on their current position and movement direction. This mechanism identifies the streets that nodes need to monitor by comparing the amount of stored pollution data with the one harvested from other nodes in the network. The Decentralised Data Fusion (DDF) algorithm is developed to fuse the delayed pollution data that arrives from other nodes in the network. The algorithm uses the Delayed State Information Filter to refine sensor measurements and applies an interpolation technique to interpolate the missing data in the time gaps which occur as a consequence of receiving delayed data. The proposed algorithm augments the pollution data history at the slight cost of data accuracy. The Decentralised Dissemination of Warnings (DDW) is a beaconless, multihop dissemination mechanism designed to relay early warnings to the SMUs. Mobile nodes calculate the time interval (waiting time) that they need to wait before they rebroadcast the warning message. The waiting time is calculated by taking into account the distance between sending and receiving nodes, and nodes’ distances to the SMUs. The DDW mechanism ensures that high number of non-duplicated warnings is received at the collection points (SMUs). The proposed framework enables efficient utilisation of node’s on-board resources in terms of transmitting and processing activity, and data storage. It reduces the number of mobile nodes required for monitoring purposes without losing the volume of the relevant collected pollution data. Using uncontrolled mobile nodes and their mobility, the framework enables reporting the hazardous pollution levels in near real-time.Open Acces

Distributed Random Set Theoretic Soft/Hard Data Fusion

Research on multisensor data fusion aims at providing the enabling technology to combine information from several sources in order to form a unifi ed picture. The literature work on fusion of conventional data provided by non-human (hard) sensors is vast and well-established. In comparison to conventional fusion systems where input data are generated by calibrated electronic sensor systems with well-defi ned characteristics, research on soft data fusion considers combining human-based data expressed preferably in unconstrained natural language form. Fusion of soft and hard data is even more challenging, yet necessary in some applications, and has received little attention in the past. Due to being a rather new area of research, soft/hard data fusion is still in a edging stage with even its challenging problems yet to be adequately de fined and explored. This dissertation develops a framework to enable fusion of both soft and hard data with the Random Set (RS) theory as the underlying mathematical foundation. Random set theory is an emerging theory within the data fusion community that, due to its powerful representational and computational capabilities, is gaining more and more attention among the data fusion researchers. Motivated by the unique characteristics of the random set theory and the main challenge of soft/hard data fusion systems, i.e. the need for a unifying framework capable of processing both unconventional soft data and conventional hard data, this dissertation argues in favor of a random set theoretic approach as the first step towards realizing a soft/hard data fusion framework. Several challenging problems related to soft/hard fusion systems are addressed in the proposed framework. First, an extension of the well-known Kalman lter within random set theory, called Kalman evidential filter (KEF), is adopted as a common data processing framework for both soft and hard data. Second, a novel ontology (syntax+semantics) is developed to allow for modeling soft (human-generated) data assuming target tracking as the application. Third, as soft/hard data fusion is mostly aimed at large networks of information processing, a new approach is proposed to enable distributed estimation of soft, as well as hard data, addressing the scalability requirement of such fusion systems. Fourth, a method for modeling trust in the human agents is developed, which enables the fusion system to protect itself from erroneous/misleading soft data through discounting such data on-the-fly. Fifth, leveraging the recent developments in the RS theoretic data fusion literature a novel soft data association algorithm is developed and deployed to extend the proposed target tracking framework into multi-target tracking case. Finally, the multi-target tracking framework is complemented by introducing a distributed classi fication approach applicable to target classes described with soft human-generated data. In addition, this dissertation presents a novel data-centric taxonomy of data fusion methodologies. In particular, several categories of fusion algorithms have been identifi ed and discussed based on the data-related challenging aspect(s) addressed. It is intended to provide the reader with a generic and comprehensive view of the contemporary data fusion literature, which could also serve as a reference for data fusion practitioners by providing them with conducive design guidelines, in terms of algorithm choice, regarding the specifi c data-related challenges expected in a given application

Object-level fusion for surround environment perception in automated driving applications

Driver assistance systems have increasingly relied on more sensors for new functions. As advanced driver assistance system continue to improve towards automated driving, new methods are required for processing the data in an efficient and economical manner from the sensors for such complex systems. The detection of dynamic objects is one of the most important aspects required by advanced driver assistance systems and automated driving. In this thesis, an environment model approach for the detection of dynamic objects is presented in order to realize an effective method for sensor data fusion. A scalable high-level fusion architecture is developed for fusing object data from several sensors in a single system, where processing occurs in three levels: sensor, fusion and application. A complete and consistent object model which includes the object’s dynamic state, existence probability and classification is defined as a sensor-independent and generic interface for sensor data fusion across all three processing levels. Novel algorithms are developed for object data association and fusion at the fusion-level of the architecture. An asynchronous sensor-to-global fusion strategy is applied in order to process sensor data immediately within the high-level fusion architecture, giving driver assistance systems the most up-to-date information about the vehicle’s environment. Track-to-track fusion algorithms are uniquely applied for dynamic state fusion, where the information matrix fusion algorithm produces results comparable to a low-level central Kalman filter approach. The existence probability of an object is fused using a novel approach based on the Dempster-Shafer evidence theory, where the individual sensor’s existence estimation performance is considered during the fusion process. A similar novel approach with the Dempster-Shafer evidence theory is also applied to the fusion of an object’s classification. The developed high-level sensor data fusion architecture and its algorithms are evaluated using a prototype vehicle equipped with 12 sensors for surround environment perception. A thorough evaluation of the complete object model is performed on a closed test track using vehicles equipped with hardware for generating an accurate ground truth. Existence and classification performance is evaluated using labeled data sets from real traffic scenarios. The evaluation demonstrates the accuracy and effectiveness of the proposed sensor data fusion approach. The work presented in this thesis has additionally been extensively used in several research projects as the dynamic object detection platform for automated driving applications on highways in real traffic