Positioning, tracking and mapping for outdoor augmentation

Karlekar, Jayashree; Zhou, Steven Zhiying; Lu, Weiquan; Loh, Zhi Chang; Nakayama, Yuta; Hii, Daniel Jun Chung

doi:10.1109/ismar.2010.5643567

Cited by 28 publications

(22 citation statements)

References 26 publications

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“…Relevant markerless solutions based on image processing [6,7] could be proposed instead, and the solution described in [8] is for instance promising on mobile platforms. However, it is not applicable in our outdoor conditions and other issues such as the important distance of targets and boat motions disqualify this approach.…”

Section: Outdoor Applicationsmentioning

confidence: 99%

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Dedicated object processor for mobile augmented reality - sailor assistance case study

2015

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This paper addresses the design of embedded systems for outdoor augmented reality (AR) applications integrated to see-through glasses. The set of tasks includes object positioning, graphic computation, as well as wireless communications, and we consider constraints such as real-time, low power, and low footprint. We introduce an original sailor assistance application, as a typical, useful, and complex outdoor AR application, where context-dependent virtual objects must be placed in the user field of view according to head motions and ambient information. Our study demonstrates that it is worth working on power optimization, since the embedded system based on a standard general-purpose processor (GPP) + graphics processing unit (GPU) consumes more than high-luminosity see-through glasses. This work presents then three main contributions, the first one is the choice and combinations of position and attitude algorithms that fit with the application context. The second one is the architecture of the embedded system, where it is introduced as a fast and simple object processor (OP) optimized for the domain of mobile AR. Finally, the OP implements a new pixel rendering method (incremental pixel shader (IPS)), which is implemented in hardware and takes full advantage of OpenGL ES light model. A GP+OP(s) complete architecture is described and prototyped on field programmable gate-array (FPGA). It includes hardware/software partitioning based on the analysis of application requirements and ergonomics.

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Section: Outdoor Applicationsmentioning

confidence: 99%

“…Rasterization and pixel coloring (6), (7). Pixel computation is the greediest task in terms of bandwidth.…”

Section: Transformed Filtered Objects (4)(5)mentioning

confidence: 99%

Dedicated object processor for mobile augmented reality - sailor assistance case study

2015

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“…Some previous approaches use a polygonal model of the outdoor environment for localization and tracking, such as the system of Reitmayr and Drummond [22] and Karlekar et al [12]. Production of such models is a time-consuming manual process.…”

Section: Related Workmentioning

confidence: 99%

Wide-area scene mapping for mobile visual tracking

Ventura

Höllerer

2012

2012 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)

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We propose a system for easily preparing arbitrary wide-area environments for subsequent real-time tracking with a handheld device. Our system evaluation shows that minimal user effort is required to initialize a camera tracking session in an unprepared environment. We combine panoramas captured using a handheld omnidirectional camera from several viewpoints to create a point cloud model. After the offline modeling step, live camera pose tracking is initialized by feature point matching, and continuously updated by aligning the point cloud model to the camera image. Given a reconstruction made with less than five minutes of video, we achieve below 25 cm translational error and 0.5 degrees rotational error for over 80% of images tested. In contrast to camera-based simultaneous localization and mapping (SLAM) systems, our methods are suitable for handheld use in large outdoor spaces.

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“…The exact implementation of the localization process depends on the type of available information. For example, Schindler et al perform localization using a database of images [15], Wu et al perform localization using image patches derived from models of the scene [18], Karlekar et al [8] use virtual building models in order to match silhouettes to aid in localization, and Yang et al [5] use high quality terrestrial LIDAR data in order to perform recovery.…”

Section: Model-based Recoverymentioning

confidence: 99%

Evaluating the impact of recovery density on augmented reality tracking

Coffin

Lee

Höllerer

2011

2011 10th IEEE International Symposium on Mixed and Augmented Reality

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Natural feature tracking systems for augmented reality are highly accurate, but can suffer from lost tracking. When registration is lost, the system must be able to re-localize and recover tracking. Likewise, when a camera is new to a scene, it must be able to perform the related task of localization. Localization and re-localization can only be performed at certain points or when viewing particular objects or parts of the scene with a sufficient number and quality of recognizable features to allow for tracking recovery. We explore how the density of such recovery locations/poses influences the time it takes users to resume tracking. We focus our evaluation on two generalized techniques for localization: keyframe-based and model-based. For the keyframe-based approach we assume a constant collection rate for keyframes. We find that at practical collection rates, the task of localization to a previously acquired keyframe that is shown to the user does not become more time-consuming as the interval between keyframes increases. For a localization approach using model data, we consider a grid of points around the model at which localization is guaranteed to succeed. We find that the user interface is crucial to successful localization. Localization can occur quickly if users do not need to orient themselves to marked localization points. When users are forced to mentally register themselves with a map of the scene, localization quickly becomes impractical as the distance to the next localization point increases. We contend that our results will help future designers of localization techniques to better plan for the effects of their proposed solutions.

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Positioning, tracking and mapping for outdoor augmentation

Cited by 28 publications

References 26 publications

Dedicated object processor for mobile augmented reality - sailor assistance case study

Dedicated object processor for mobile augmented reality - sailor assistance case study

Wide-area scene mapping for mobile visual tracking

Evaluating the impact of recovery density on augmented reality tracking

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