3D Town: The Automatic Urban Awareness Project

Corral-Soto, Eduardo R.; Tal, Ron; Wang, Langyue; Persad, Ravi Ancil; Luo, Chao; Solomon, Chan; Hou, Bob L.; Sohn, Gunho; Elder, James H.

doi:10.1109/crv.2012.64

Cited by 13 publications

(8 citation statements)

References 17 publications

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“…The model was generated from building footprint vector data, Digital Surface Model (DSM) with 0.75m ground spacing, corresponding orthophotos at 0.15 m spatial resolution and terrestrial images. The 3D building models were further refined with airborne lidar data having a point density of 1.9 points per square metre (Corral-Soto et al, 2012). The 3D CAD model serves two purposes in the proposed approach.…”

Section: Resultsmentioning

confidence: 99%

A FEASIBILITY STUDY ON USING ViSP’S 3D MODEL-BASED TRACKER FOR UAV POSE ESTIMATION IN OUTDOOR ENVIRONMENTS

Li-Chee-Ming

Armenakis

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission I, ICWG I/VbKEYWORDS: UAV, Close Range Photogrammetry, Mapping, Tracking, Robotics, Navigation, Object Recognition, Augmented Reality, ViSP ABSTRACT:This paper presents a novel application of the Visual Servoing Platform's (ViSP) for small UAV pose estimation in outdoor environments. Given an initial approximation for the camera position and orientation, or camera pose, ViSP automatically establishes and continuously tracks corresponding features between an image sequence and a 3D wireframe model of the environment. As ViSP has been demonstrated to perform well in small and cluttered indoor environments, this paper explores the application of ViSP for UAV mapping of outdoor landscapes and tracking of large objects (i.e. building models). Our presented experiments demonstrate the data obtainable by the UAV, assess ViSP's data processing strategies, and evaluate the performance of the tracker.

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Section: Resultsmentioning

confidence: 99%

A FEASIBILITY STUDY ON USING ViSP’S 3D MODEL-BASED TRACKER FOR UAV POSE ESTIMATION IN OUTDOOR ENVIRONMENTS

Li-Chee-Ming

Armenakis

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Ref. [95] employed a geospatial webserver to create 3D views for a 3D town project. This server allows the combination of various sources, such as urban 3D models stored in the geo-server and sensor information for tracking the urban mobility to visualize the urban change events, such as traffic.…”

Section: Server Issuesmentioning

confidence: 99%

A Review on Mixed Reality: Current Trends, Challenges and Prospects

2020

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Currently, new technologies have enabled the design of smart applications that are used as decision-making tools in the problems of daily life. The key issue in designing such an application is the increasing level of user interaction. Mixed reality (MR) is an emerging technology that deals with maximum user interaction in the real world compared to other similar technologies. Developing an MR application is complicated, and depends on the different components that have been addressed in previous literature. In addition to the extraction of such components, a comprehensive study that presents a generic framework comprising all components required to develop MR applications needs to be performed. This review studies intensive research to obtain a comprehensive framework for MR applications. The suggested framework comprises five layers: the first layer considers system components; the second and third layers focus on architectural issues for component integration; the fourth layer is the application layer that executes the architecture; and the fifth layer is the user interface layer that enables user interaction. The merits of this study are as follows: this review can act as a proper resource for MR basic concepts, and it introduces MR development steps and analytical models, a simulation toolkit, system types, and architecture types, in addition to practical issues for stakeholders such as considering MR different domains.

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“…Monocular visual SLAM has been widely used by UAVs in outdoor environments. Suitable open-source methods that process RGB image sequences include parallel tracking and mapping (PTAM) (Klein and Murray 2007), large-scale direct monocular (LSD) SLAM (Engel, Schops, and Cremers 2014), and Oriented FAST and Rotated BRIEF (ORB) -SLAM (Mur-Artal, Montiel, and were further refined with airborne lidar data having a point density of 1.9 points per square meter (Corral-Soto et al 2012). The 3D CAD model served two purposes in the proposed approach.…”

Section: Data Processingmentioning

confidence: 99%

UAV navigation system using line-based sensor pose estimation

Li-Chee-Ming

Armenakis

2018

Geo-spatial Information Science

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This work presents a mapping and tracking system based on images to enable a small Unmanned Aerial Vehicle (UAV) to accurately navigate in indoor and GPS-denied outdoor environments. A method is proposed to estimate the UAV's pose (i.e., the 3D position and orientation of the camera sensor) in real-time using only the on-board RGB camera as the UAV travels through a known 3D environment (i.e., a 3D CAD model). Linear features are extracted and automatically matched between images collected by the UAV's onboard RGB camera and the 3D object model. The matched lines from the 3D model serve as ground control to estimate the camera pose in real-time via line-based space resection. The results demonstrate that the proposed modelbased pose estimation algorithm provides sub-meter positioning accuracies in both indoor and outdoor environments. It is also that shown the proposed method can provide sparse updates to correct the drift from complementary simultaneous localization and mapping (SLAM)-derived pose estimates.

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3D Town: The Automatic Urban Awareness Project

Cited by 13 publications

References 17 publications

A FEASIBILITY STUDY ON USING ViSP’S 3D MODEL-BASED TRACKER FOR UAV POSE ESTIMATION IN OUTDOOR ENVIRONMENTS

A FEASIBILITY STUDY ON USING ViSP’S 3D MODEL-BASED TRACKER FOR UAV POSE ESTIMATION IN OUTDOOR ENVIRONMENTS

A Review on Mixed Reality: Current Trends, Challenges and Prospects

UAV navigation system using line-based sensor pose estimation

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