Effective erasure codes for reliable computer communication protocols
ACM SIGCOMM Computer Communication Review
ACM Transactions on Computer-Human Interaction (TOCHI)
TRIP: A Low-Cost Vision-Based Location System for Ubiquitous Computing
Personal and Ubiquitous Computing
The MagicBook—Moving Seamlessly between Reality and Virtuality
IEEE Computer Graphics and Applications
Matrix: A Realtime Object Identification and Registration Method for Augmented Reality
APCHI '98 Proceedings of the Third Asian Pacific Computer and Human Interaction
Evaluating lateration-based positioning algorithms for fine-grained tracking
DIALM-POMC '05 Proceedings of the 2005 joint workshop on Foundations of mobile computing
Dependable coding of fiducial tags
UCS'04 Proceedings of the Second international conference on Ubiquitous Computing Systems
Towards massively multi-user augmented reality on handheld devices
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
TrackSense: infrastructure free precise indoor positioning using projected patterns
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
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Fiducial scene markers provide inexpensive vision-based location systems that are of increasing interest to the Pervasive Computing community. Already established in the Augmented Reality (AR) field, markers are cheap to print and straightforward to locate in three dimensions. When used as a component of a smart environment, however, there are issues of obscuration, insufficient camera resolution and limited numbers of unique markers. This paper looks at the advantages of clustering multiple markers together to gain resilience to these real world problems. It treats the visual channel as an erasure channel and relevant coding schemes are applied to decode data that is distributed across the marker cluster using an algorithm that does not require each tag to be individually numbered. The advantages of clustering are determined to be a resilience to obscuration, more robust position and pose determination, better performance when attached to inconvenient shapes, and an ability to encode more than a database key into the environment. A real world example comparing the positioning capabilities of a cluster of tags with that of a single tag is presented. It is apparent that clustering provides a position estimate that is more robust, without requiring external definition of a co-ordinate frame using a database.