Browsing by Subject "Dynamic SLAM"

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    Registration and Localization of Unknown Moving Objects in Markerless Monocular SLAM
    (2023-05) Troutman, Blake; Tuceryan, Mihran; Fang, Shiaofen; Tsechpenakis, Gavriil; Hu, Qin
    Simultaneous localization and mapping (SLAM) is a general device localization technique that uses realtime sensor measurements to develop a virtualization of the sensor's environment while also using this growing virtualization to determine the position and orientation of the sensor. This is useful for augmented reality (AR), in which a user looks through a head-mounted display (HMD) or viewfinder to see virtual components integrated into the real world. Visual SLAM (i.e., SLAM in which the sensor is an optical camera) is used in AR to determine the exact device/headset movement so that the virtual components can be accurately redrawn to the screen, matching the perceived motion of the world around the user as the user moves the device/headset. However, many potential AR applications may need access to more than device localization data in order to be useful; they may need to leverage environment data as well. Additionally, most SLAM solutions make the naive assumption that the environment surrounding the system is completely static (non-moving). Given these circumstances, it is clear that AR may benefit substantially from utilizing a SLAM solution that detects objects that move in the scene and ultimately provides localization data for each of these objects. This problem is known as the dynamic SLAM problem. Current attempts to address the dynamic SLAM problem often use machine learning to develop models that identify the parts of the camera image that belong to one of many classes of potentially-moving objects. The limitation with these approaches is that it is impractical to train models to identify every possible object that moves; additionally, some potentially-moving objects may be static in the scene, which these approaches often do not account for. Some other attempts to address the dynamic SLAM problem also localize the moving objects they detect, but these systems almost always rely on depth sensors or stereo camera configurations, which have significant limitations in real-world use cases. This dissertation presents a novel approach for registering and localizing unknown moving objects in the context of markerless, monocular, keyframe-based SLAM with no required prior information about object structure, appearance, or existence. This work also details a novel deep learning solution for determining SLAM map initialization suitability in structure-from-motion-based initialization approaches. This dissertation goes on to validate these approaches by implementing them in a markerless, monocular SLAM system called LUMO-SLAM, which is built from the ground up to demonstrate this approach to unknown moving object registration and localization. Results are collected for the LUMO-SLAM system, which address the accuracy of its camera localization estimates, the accuracy of its moving object localization estimates, and the consistency with which it registers moving objects in the scene. These results show that this solution to the dynamic SLAM problem, though it does not act as a practical solution for all use cases, has an ability to accurately register and localize unknown moving objects in such a way that makes it useful for some applications of AR without thwarting the system's ability to also perform accurate camera localization.
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    Registration and Localization of Unknown Moving Objects in Monocular SLAM
    (IEEE, 2022-03-23) Troutman, Blake; Tuceryan, Mihran; Computer and Information Science, School of Science
    Augmented reality (AR) applications require constant device localization, which is often fulfilled by visual simultaneous localization and mapping (SLAM). SLAM provides realtime camera localization by also dynamically building a 3D map of the environment, but the functionality of SLAM systems generally stops here. Useful applications of AR could make great use of additional information about the environment, such as the structure and location of moving objects in the scene (including objects that were not previously known to be separate from the static points of the map). We present an approach for solving the visual SLAM problem while also registering and localizing moving objects without prior knowledge of the objects’ structure, appearance, or existence. This is accomplished via analysis of reprojection errors and iterative use of the ePnP algorithm in a RANSAC scheme. The approach is demonstrated with the accompanying prototype system, LUMO-SLAM. The initial results achieved by this system indicate that the approach is both sound and potentially viable for some practical applications of AR and visual SLAM.
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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.