F UTURE W ORKS

Some directions for future study are recommended below:

For the localization environment, a distributed version will be designed and implemented.

The position of the reference node will be saved and sent to the mobile node in the localization

phase. Location-aware computation can be performed on the ZigBee module. Furthermore, the proposed method estimates the location from only a set of RSSI values without using previous RSSI values. Thus it is also interesting to examine the performance of the target tracking with the proposed method. Furthermore, the system can be applied to a “never-lost” system, while not only human, but also any object which is RF-tagged in the house. They can be found through location aware system and visualized on a TV set, for instance.

For the MonoVNS, we will work on reducing the system execution time in order to use high resolution imagery. The computation intensive parts of the proposed method are feature extraction and matching. It can be further improved by using other feature descriptors or by using a GPU to accelerate. In the future, we will also investigate solutions for a wider camera view angle, for instance, by using a wide-angle lens or pan-tilt platform.

For the integrated location aware system, the mobile robot and the intelligent environment should be able to cooperate more thoroughly. For instance, the robot should access the camera in the environment for self-localization and human tracking. The object with ZigBee modules or RF-tagged should also be fetched by the robot.

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