Browsing by Subject "SLAM"

Now showing 1 - 5 of 5
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    Acoustic Simultaneous Localization And Mapping (SLAM)
    (2021-12) Madan, Akul; Li, Lingxi; Chen, Yaobin; King, Brian
    The current technologies employed for autonomous driving provide tremendous performance and results, but the technology itself is far from mature and relatively expensive. Some of the most commonly used components for autonomous driving include LiDAR, cameras, radar, and ultrasonic sensors. Sensors like such are usually high-priced and often require a tremendous amount of computational power in order to process the gathered data. Many car manufacturers consider cameras to be a low-cost alternative to some other costly sensors, but camera based sensors alone are prone to fatal perception errors. In many cases, adverse weather and night-time conditions hinder the performance of some vision based sensors. In order for a sensor to be a reliable source of data, the difference between actual data values and measured or perceived values should be as low as possible. Lowering the number of sensors used provides more economic freedom to invest in the reliability of the components used. This thesis provides an alternative approach to the current autonomous driving methodologies by utilizing acoustic signatures of moving objects. This approach makes use of a microphone array to collect and process acoustic signatures captured for simultaneous localization and mapping (SLAM). Rather than using numerous sensors to gather information about the surroundings that are beyond the reach of the user, this method investigates the benefits of considering the sound waves of different objects around the host vehicle for SLAM. The components used in this model are cost-efficient and generate data that is easy to process without requiring high processing power. The results prove that there are benefits in pursuing this approach in terms of cost efficiency and low computational power. The functionality of the model is demonstrated using MATLAB for data collection and testing.
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    Data Acquisition and Processing Pipeline for E-Scooter Tracking Using 3d Lidar and Multi-Camera Setup
    (2020-12) Betrabet, Siddhant S.; Tian, Renran; Zhu, Likun; Anwar, Sohel
    Analyzing behaviors of objects on the road is a complex task that requires data from various sensors and their fusion to recreate the movement of objects with a high degree of accuracy. A data collection and processing system are thus needed to track the objects accurately in order to make an accurate and clear map of the trajectories of objects relative to various coordinate frame(s) of interest in the map. Detection and tracking moving objects (DATMO) and Simultaneous localization and mapping (SLAM) are the tasks that needs to be achieved in conjunction to create a clear map of the road comprising of the moving and static objects. These computational problems are commonly solved and used to aid scenario reconstruction for the objects of interest. The tracking of objects can be done in various ways, utilizing sensors such as monocular or stereo cameras, Light Detection and Ranging (LIDAR) sensors as well as Inertial Navigation systems (INS) systems. One relatively common method for solving DATMO and SLAM involves utilizing a 3D LIDAR with multiple monocular cameras in conjunction with an inertial measurement unit (IMU) allows for redundancies to maintain object classification and tracking with the help of sensor fusion in cases when sensor specific traditional algorithms prove to be ineffectual when either sensor falls short due to their limitations. The usage of the IMU and sensor fusion methods relatively eliminates the need for having an expensive INS rig. Fusion of these sensors allows for more effectual tracking to utilize the maximum potential of each sensor while allowing for methods to increase perceptional accuracy. The focus of this thesis will be the dock-less e-scooter and the primary goal will be to track its movements effectively and accurately with respect to cars on the road and the world. Since it is relatively more common to observe a car on the road than e-scooters, we propose a data collection system that can be built on top of an e-scooter and an offline processing pipeline that can be used to collect data in order to understand the behaviors of the e-scooters themselves. In this thesis, we plan to explore a data collection system involving a 3D LIDAR sensor and multiple monocular cameras and an IMU on an e-scooter as well as an offline method for processing the data to generate data to aid scenario reconstruction.
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    An Experimental Distributed Framework for Distributed Simultaneous Localization and Mapping
    (IEEE, 2016-05) Gamage, Ruwan; Tuceryan, Mihran; Department of Computer and Information Science, School of Science
    Simultaneous Localization and Mapping (SLAM) is widely used in applications such as rescue, navigation, semantic mapping, augmented reality and home entertainment applications. Most of these applications would do better if multiple devices are used in a distributed setting. The distributed SLAM research would benefit if there is a framework where the complexities of network communication is already handled. In this paper we introduce such framework utilizing open source Robot Operating System (ROS) and VirtualBox virtualization software. Furthermore, we describe a way to measure communication statistics of the distributed SLAM system.
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    Hematopoietic Stem Cell Identification Postirradiation
    (Springer, 2023) Patterson, Andrea M.; Orschell, Christie M.; Pelus, Louis M.; Medicine, School of Medicine
    Radiation exposure is particularly damaging to cells of the hematopoietic system, inducing pancytopenia and bone marrow failure. The study of these processes, as well as the development of treatments to prevent hematopoietic damage or enhance recovery after radiation exposure, often require analysis of bone marrow cells early after irradiation. While flow cytometry methods are well characterized for identification and analysis of bone marrow populations in the nonirradiated setting, multiple complications arise when dealing with irradiated tissues. Among these complications is a radiation-induced loss of c-Kit, a central marker for conventional gating of primitive hematopoietic populations in mice. These include hematopoietic stem cells (HSCs), which are central to blood reconstitution and life-long bone marrow function, and are important targets of analysis in these studies. This chapter outlines techniques for HSC identification and analysis from mouse bone marrow postirradiation.
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    Towards Dynamic Realtime Object Labeling in Augmented Reality
    (IEEE, 2022-12) Troutman, Blake; Tuceryan, Mihran; Computer and Information Science, School of Science
    The applicability of augmented reality (AR) is stunted by the current limitations of localization systems. In various forms, simultaneous localization and mapping (SLAM) has become a common framework for providing device localization in AR systems; however, outside of camera localization data, SLAM systems typically fail to provide additional information about the environment to consumer applications. This limits the domain of potential AR applications, as many applications will require some degree of interaction between the real and virtual worlds. One such application is object labeling for moving objects. In this work, we implement an AR moving object labeling system by utilizing LUMO-SLAM, a SLAM system that registers and localizes unknown moving objects in the environment. Test runs of the system show that moving object information provided by LUMO-SLAM is sufficient for implementing a useful moving object labeling system and potentially other real-world applications of AR.