<title>Development of a sensor integration strategy for robotic application based on geometric optimization</title>

摘要

Sensor fusion is an important technology, which is growing exponentially due to its tremendous application potential. Appropriate fusion technology is needed to be developed specially when a system requires redundant sensors to be used. The more the redundancy in sensors, the more the computational complexity for controlling the system and the more is its intelligence level. This research presents a strategy developed for multiple sensor fusion, based on geometric optimization. Each sensor's uncertainty model has been developed. Using Lagrangian optimization techniques the individual sensor's uncertainty has been fused to reduce the overall uncertainty to generate a consensus among the sensors regarding their acceptable values. Using fission-fusion architecture, the precision level has further been improved. Subsequently, using feed back from the fused sensory information, the net error has further been minimized to any pre assigned value by developing a fusion technique in the differential domain (FDD). The techniques have been illustrated using synthesized data from two types of sensors (optical encoder and a single camera vision sensor). The application experience of the same fusion strategy in improving the precision of correctness of stereo matching using multiple baselines has also been discussed.