Mixed reality training systems using Head Mounted Displays (HMDs) require very high precision knowledge of the 3D
location and 3D orientation of the user's head. This is required by the system to know where to insert the synthetic
actors and objects in the HMD. The inserted objects must appear stable and not jitter or drift. Moreover latency of less
than 5 milliseconds for pose estimation is required for lag-free see-through HMD operation. We describe how to achieve
this performance using a multi-camera based visual navigation system mounted on the HMD. A Kalman filter is used to
integrate high rate estimates from an IMU with a visual odometry system and to predict head motion. Landmark
matching and GPS when available are used to correct any drifts.
Traditional vision-based navigation system often drifts over time during navigation. In this paper, we propose
a set of techniques which greatly reduce the long term drift and also improve its robustness to many failure
conditions. In our approach, two pairs of stereo cameras are integrated to form a forward/backward multi-stereo
camera system. As a result, the Field-Of-View of the system is extended significantly to capture more
natural landmarks from the scene. This helps to increase the pose estimation accuracy as well as reduce the
failure situations. Secondly, a global landmark matching technique is used to recognize the previously visited
locations during navigation. Using the matched landmarks, a pose correction technique is used to eliminate the
accumulated navigation drift. Finally, in order to further improve the robustness of the system, measurements
from low-cost Inertial Measurement Unit (IMU) and Global Positioning System (GPS) sensors are integrated
with the visual odometry in an extended Kalman Filtering framework. Our system is significantly more accurate
and robust than previously published techniques (1∼5% localization error) over long-distance navigation both
indoors and outdoors. Real world experiments on a human worn system show that the location can be estimated
within 1 meter over 500 meters (around 0.1% localization error averagely) without the use of GPS information.
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