Do everything for life!
Do everything for life!
Do everything for life!
Do everything for life!
Do everything for life!
Do everything for life!
Do everything for life!
Do everything for life!
Do everything for life!
Do everything for life!
Do everything for life!
Do everything for life!

Nafosted-2016-2018, funded by the National Foundation

Development of an intelligent system for ambient understanding applied to autonomous navigation in outdoor environment.
This project proposes and studies a novel method that improves efficiency of scene understanding, which is useful for autonomous navigation, based on the multiple sensors-based. The method is expected to provide the better scene understanding, which then allows a robot to avoid obstacles in front of its navigation. The method consists of some stages. First, a path planning is investigated, which concerns about the findings of the efficient path to facilitate the autonomous driving. The second is referred to the problem of the motion estimation and the localization prediction of running vehicle. It is addressed based on applying the fusion of cameras, LRF and GPS devices. Our research proposes a new method that uses the minimal set of parameters consisting of the geometrical constraints for estimating the 3D motion of vehicle using cameras and LRF. The cumulative errors of visual odometry are excluded using the GPS-based correction relied on the maximum likelihood estimation in particle filter. The semantic of object classification and detection are also studied. The obstacle detection, place recognition techniques are used as a solution to assisting the autonomous driver. We focus on dealing with detecting obstacles that commonly occur such as pedestrians and vehicles. The place recognition is also considered for scene understanding and mapping. We introduce improving of feature descriptors, deformable part model as well hybrid boosting SVM and neural network.
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