A System Architecture for Ubiquitous Tracking Environments

Huber, Manuel; Pustka, Daniel; Keitler, Peter; Echtler, Florian; Klinker, Gudrun

doi:10.1109/ismar.2007.4538849

Cited by 38 publications

(17 citation statements)

References 10 publications

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“…The acquisition of measurement data was carried out with our Ubitrack framework [9] with the aid of the trackman configuration tool [13]. Data was evaluated offline with Mathematica 2 .…”

Section: Evaluation and Resultsmentioning

confidence: 99%

Indirect Tracking to Reduce Occlusion Problems

Keitler

Schlegel

Klinker

2008

Advances in Visual Computing

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Abstract. There exist many infrared inside-out 6-DOF pose tracking configurations with cameras mounted rigidly to the environment. In such a setup, tracking is inherently impossible for IR targets inside/below/behind other opaque objects (occlusion problem). We present a solution for the integration of an additional, mobile IR tracking system to overcome this problem. The solution consists of an indirect tracking setup where the stationary cameras track the mobile cameras which in turn track the target. Accuracy problems that are inherent to such an indirect tracking setup, are tackled by an error correction mechanism based on reference points in the scene that are known to both tracking systems. An evaluation demonstrates that, in naive indirect tracking without error correction, the major source of error consists in a wrong detection of orientation of the mobile system and that this source of error can be practically eliminated by our error correction mechanisms. Keywords: Augmented Reality, indirect tracking, sensor fusion, absolute orientation problem. MotivationOne of the currently most common tracking setups for AR and VR applications consists of an outside-in configuration with a number of infrared cameras mounted rigidly to the environment, observing a fixed volume within their midst. The camera arrangement imposes restrictions toward tracking moveable objects inside/below/behind other opaque objects in the scene. We call this the occlusion problem. It is not generally solvable by adding additional cameras to the classical outside-in setup since first, occlusions generated by scene objects cannot always be known in advance and second, the scene may offer only small and varying viewing angles to the outside, which cannot simply be covered by adding some more cameras. This is especially true for trackable objects surrounded by other objects, e.g. a tool inside a car body.Our indirect tracking approach adds an additional, mobile IR tracking system which can be placed in the scene on-the-fly such that it can see trackable objects that are hidden to the stationary cameras. The mobile setup itself is equipped with a marker so that its pose can be tracked by the stationary setup (see Figure 1(a)). Figure 1(b) shows how the proposed solution would look like in AR-stud-welding, one of our industrial scenarios where the stud-welding gun has to be tracked inside the car body so that navigational information about the next welding position can be shown on a display attached to the welding gun. This application is already in productive use but until now, it suffers from the restrictions of classical outside-in tracking described above.

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“…The acquisition of measurement data was carried out with our Ubitrack framework [9] with the aid of the trackman configuration tool [13]. Data was evaluated offline with Mathematica 2 .…”

Section: Evaluation and Resultsmentioning

confidence: 99%

Indirect Tracking to Reduce Occlusion Problems

Keitler

Schlegel

Klinker

2008

Advances in Visual Computing

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“…One of the major remaining questions in dynamic reconfigurable tracking setups (such as [5]) is how to dynamically account for sensor synchronization. The Ubitrack framework so far accounts for unsynchronized sensors by utilizing a Push/Pull dataflow architecture [7] which depends on the correctness of timestamps associated with sensor measurements.…”

Section: Related Workmentioning

confidence: 99%

“…Mostly components are either hardware synchronized or the lag between different sensors is tuned in software by experimental means. Drawbacks of these approaches are that common off-the-shelf hardware often lacks hardware synchronization interfaces and that dynamic sensor fusion as proposed in [5] requires methods for automatic adjustments.…”

Section: Introductionmentioning

confidence: 99%

Temporal calibration in multisensor tracking setups

Huber

Schlegel

Klinker

2009

2009 8th IEEE International Symposium on Mixed and Augmented Reality

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Spatial tracking is one of the most challenging parts of Augmented Reality. Many AR applications rely on the fusion of several tracking systems in order to optimize the overall performance. While the topic of sensor fusion has already seen considerable interest, most results only deal with the integration of particular setups.A crucial part of sensor fusion is the temporal alignment of the sensor signals, as sensors in general are not synchronized. We present a general method to calibrate the temporal offset between different sensors by applying the normalized cross correlation method.

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“…Ubitrack can use pattern matching techniques to automatically derive a DFG from a given SRG [9], [17] (A2). AR-engineers can influence the creation of DFGs manually in trackman, supporting requirements R2 and R4 (A2).…”

Section: Srg Design Activitiesmentioning

confidence: 99%

“…It first deduces a transitive transformation from B to D (Multiplication), then converts the mode of transformation A to B from push to pull and finally concatenates both to the desired result. On the opposite side of options, fully automatic pattern matching is also offered by Ubitrack [9]. Yet it has its limitations in selecting optimal patterns for every purpose.…”

Section: Semi-automatic Modelingmentioning

confidence: 99%

Management of Tracking for Mixed and Augmented Reality Systems

Keitler

Pustka

Huber

et al. 2009

The Engineering of Mixed Reality Systems

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Position and orientation tracking is a major challenge for Mixed / Augmented Reality applications, especially in heterogeneous and wide-area sensor setups. In this article, we describe trackman, a planning and analysis tool which supports the AR-engineer in setup and maintenance of the tracking infrastructure. A new graphical modeling approach based on spatial relationship graphs (SRGs) eases the specification of known as well as the deduction of new relationships between entities in the scene. Modeling is based on reusable patterns representing the underlying sensor drivers or algorithms. Recurring constellations in the scene can be condensed into reusable meta-patterns. The process is further simplified by semiautomatic modeling techniques which automize trivial steps. Dataflow networks can be generated automatically from the SRG and are guaranteed to be semantically correct. Furthermore, generic tools are described that allow for the calibration/registration of static spatial transformations as well as for the live surveillance of tracking accuracy. In summary, this approach reduces tremendously the amount of expert knowledge needed for the administration of tracking setups.

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A System Architecture for Ubiquitous Tracking Environments

Cited by 38 publications

References 10 publications

Indirect Tracking to Reduce Occlusion Problems

Indirect Tracking to Reduce Occlusion Problems

Temporal calibration in multisensor tracking setups

Management of Tracking for Mixed and Augmented Reality Systems

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