Mixed Reality Browsers and Pedestrian Navigation in Augmented Cities

International audienceIn this paper, We use a declarative format for positional audio with synchronization between audio chunks using SMIL. This format has been specifically designed for the type of audio used in AR applications. The audio engine associated to this format is running on mobile platforms (iOS, Android). Our MRB browser called IXE use a format based on volunteered geographic information (OpenStreetMap) and OSM documents for IXE can be fully authored in side OSM editors like JOSM. This is in contrast with the other AR browsers like Layar, Juniao, Wikitude, which use a Point of Interest (POI) based format having no notion of ways. This introduces a fundamental difference and in some senses a duality relation between IXE and the other AR browsers. In IXE, Augmented Virtuality (AV) navigation along a route (composed of ways) is central and AR interaction with objects is delegated to associate 3D activities. In AR browsers, navigation along a route is delegated to associated map activities and AR interaction with objects is central. IXE supports multiple tracking technologies and therefore allows both indoor navigation in buildings and outdoor navigation at the level of sidewalks. A first android version of the IXE browser will be released at the end of 2013. Being based on volunteered geographic it will allow building accessible pedestrian networks in augmented cities

Mixed Reality Browsers and Pedestrian Navigation in Augmented Cities

International audienceIn this paper, We use a declarative format for positional audio with synchronization between audio chunks using SMIL. This format has been specifically designed for the type of audio used in AR applications. The audio engine associated to this format is running on mobile platforms (iOS, Android). Our MRB browser called IXE use a format based on volunteered geographic information (OpenStreetMap) and OSM documents for IXE can be fully authored in side OSM editors like JOSM. This is in contrast with the other AR browsers like Layar, Juniao, Wikitude, which use a Point of Interest (POI) based format having no notion of ways. This introduces a fundamental difference and in some senses a duality relation between IXE and the other AR browsers. In IXE, Augmented Virtuality (AV) navigation along a route (composed of ways) is central and AR interaction with objects is delegated to associate 3D activities. In AR browsers, navigation along a route is delegated to associated map activities and AR interaction with objects is central. IXE supports multiple tracking technologies and therefore allows both indoor navigation in buildings and outdoor navigation at the level of sidewalks. A first android version of the IXE browser will be released at the end of 2013. Being based on volunteered geographic it will allow building accessible pedestrian networks in augmented cities

Combined Learned and Classical Methods for Real-Time Visual Perception in Autonomous Driving

Autonomy, robotics, and Artificial Intelligence (AI) are among the main defining themes of next-generation societies. Of the most important applications of said technologies is driving automation which spans from different Advanced Driver Assistance Systems (ADAS) to full self-driving vehicles. Driving automation is promising to reduce accidents, increase safety, and increase access to mobility for more people such as the elderly and the handicapped. However, one of the main challenges facing autonomous vehicles is robust perception which can enable safe interaction and decision making. With so many sensors to perceive the environment, each with its own capabilities and limitations, vision is by far one of the main sensing modalities. Cameras are cheap and can provide rich information of the observed scene. Therefore, this dissertation develops a set of visual perception algorithms with a focus on autonomous driving as the target application area. This dissertation starts by addressing the problem of real-time motion estimation of an agent using only the visual input from a camera attached to it, a problem known as visual odometry. The visual odometry algorithm can achieve low drift rates over long-traveled distances. This is made possible through the innovative local mapping approach used. This visual odometry algorithm was then combined with my multi-object detection and tracking system. The tracking system operates in a tracking-by-detection paradigm where an object detector based on convolution neural networks (CNNs) is used. Therefore, the combined system can detect and track other traffic participants both in image domain and in 3D world frame while simultaneously estimating vehicle motion. This is a necessary requirement for obstacle avoidance and safe navigation. Finally, the operational range of traditional monocular cameras was expanded with the capability to infer depth and thus replace stereo and RGB-D cameras. This is accomplished through a single-stream convolution neural network which can output both depth prediction and semantic segmentation. Semantic segmentation is the process of classifying each pixel in an image and is an important step toward scene understanding. Literature survey, algorithms descriptions, and comprehensive evaluations on real-world datasets are presented.Ph.D.College of Engineering & Computer ScienceUniversity of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/153989/1/Mohamed Aladem Final Dissertation.pdfDescription of Mohamed Aladem Final Dissertation.pdf : Dissertatio

Use Of Smartphones for Ensuring Vulnerable Road User Safety through Path Prediction and Early Warning: An In-Depth Review of Capabilities, Limitations and Their Applications in Cooperative Intelligent Transport Systems

The field of cooperative intelligent transport systems and more specifically pedestrians to vehicles could be characterized as quite challenging, since there is a broad research area to be studied, with direct positive results to society. Pedestrians to vehicles is a type of cooperative intelligent transport system, within the group of early warning collision/safety system. In this article, we examine the research and applications carried out so far within the field of pedestrians to vehicles cooperative transport systems by leveraging the information coming from vulnerable road users’ smartphones. Moreover, an extensive literature review has been carried out in the fields of vulnerable road users outdoor localisation via smartphones and vulnerable road users next step/movement prediction, which are closely related to pedestrian to vehicle applications and research. We identify gaps that exist in these fields that could be improved/extended/enhanced or newly developed, while we address future research objectives and methodologies that could support the improvement/development of those identified gaps

Advanced Map Matching Technologies and Techniques for Pedestrian/Wheelchair Navigation

Due to the constantly increasing technical advantages of mobile devices (such as smartphones), pedestrian/wheelchair navigation recently has achieved a high level of interest as one of smartphones’ potential mobile applications. While vehicle navigation systems have already reached a certain level of maturity, pedestrian/wheelchair navigation services are still in their infancy. By comparing vehicle navigation systems, a set of map matching requirements and challenges unique in pedestrian/wheelchair navigation is identified. To provide navigation assistance to pedestrians and wheelchair users, there is a need for the design and development of new map matching techniques. The main goal of this research is to investigate and develop advanced map matching technologies and techniques particular for pedestrian/wheelchair navigation services. As the first step in map matching, an adaptive candidate segment selection algorithm is developed to efficiently find candidate segments. Furthermore, to narrow down the search for the correct segment, advanced mathematical models are applied. GPS-based chain-code map matching, Hidden Markov Model (HMM) map matching, and fuzzy-logic map matching algorithms are developed to estimate real-time location of users in pedestrian/wheelchair navigation systems/services. Nevertheless, GPS signal is not always available in areas with high-rise buildings and even when there is a signal, the accuracy may not be high enough for localization of pedestrians and wheelchair users on sidewalks. To overcome these shortcomings of GPS, multi-sensor integrated map matching algorithms are investigated and developed in this research. These algorithms include a movement pattern recognition algorithm, using accelerometer and compass data, and a vision-based positioning algorithm to fill in signal gaps in GPS positioning. Experiments are conducted to evaluate the developed algorithms using real field test data (GPS coordinates and other sensors data). The experimental results show that the developed algorithms and the integrated sensors, i.e., a monocular visual odometry, a GPS, an accelerometer, and a compass, can provide high-quality and uninterrupted localization services in pedestrian/wheelchair navigation systems/services. The map matching techniques developed in this work can be applied to various pedestrian/wheelchair navigation applications, such as tracking senior citizens and children, or tourist service systems, and can be further utilized in building walking robots and automatic wheelchair navigation systems

3D Perception Based Lifelong Navigation of Service Robots in Dynamic Environments

Lifelong navigation of mobile robots is to ability to reliably operate over extended periods of time in dynamically changing environments. Historically, computational capacity and sensor capability have been the constraining factors to the richness of the internal representation of the environment that a mobile robot could use for navigation tasks. With affordable contemporary sensing technology available that provides rich 3D information of the environment and increased computational power, we can increasingly make use of more semantic environmental information in navigation related tasks.A navigation system has many subsystems that must operate in real time competing for computation resources in such as the perception, localization, and path planning systems. The main thesis proposed in this work is that we can utilize 3D information from the environment in our systems to increase navigational robustness without making trade-offs in any of the real time subsystems. To support these claims, this dissertation presents robust, real world 3D perception based navigation systems in the domains of indoor doorway detection and traversal, sidewalk-level outdoor navigation in urban environments, and global localization in large scale indoor warehouse environments.The discussion of these systems includes methods of 3D point cloud based object detection to find respective objects of semantic interest for the given navigation tasks as well as the use of 3D information in the navigational systems for purposes such as localization and dynamic obstacle avoidance. Experimental results for each of these applications demonstrate the effectiveness of the techniques for robust long term autonomous operation

An Overview about Emerging Technologies of Autonomous Driving

Since DARPA started Grand Challenges in 2004 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications. This paper gives an overview about technical aspects of autonomous driving technologies and open problems. We investigate the major fields of self-driving systems, such as perception, mapping and localization, prediction, planning and control, simulation, V2X and safety etc. Especially we elaborate on all these issues in a framework of data closed loop, a popular platform to solve the long tailed autonomous driving problems