This paper presents a novel state estimation system for unmanned aerial vehicle landing. A novel vision algorithm that detects a portion of the marker is developed, and this algorithm extends the detectable range of t...
详细信息
This paper presents a novel state estimation system for unmanned aerial vehicle landing. A novel vision algorithm that detects a portion of the marker is developed, and this algorithm extends the detectable range of the vision system for any known marker. A vision-aided navigation algorithm is derived within extended Kalman particle filter and Rao-Blackwellized particle filter frameworks in addition to a standard extended Kalman filter framework. These multihypothesis approaches not only deal well with a highly nonlinear and non-Gaussian distribution of the measurement errors of vision but also result in numerically stable filters. The computational costs are reduced compared to a naive implementation of particle filter, and these algorithms run in real time. This system is validated through numerical simulation, image-in-the-loop simulation, and flight tests.
A land vehicle tracking and monitoring system based on the integration of differential global position system (DGPS), dead-reckoning (DR), and map matched technology is studied. In this paper, from the economic point ...
详细信息
A land vehicle tracking and monitoring system based on the integration of differential global position system (DGPS), dead-reckoning (DR), and map matched technology is studied. In this paper, from the economic point of view, a new scheme using the one-way directional communication link, is presented. Moreover, 8-state Kalman filter is proposed for integrated DGPS/DR system. When field tests are carried out using two C/A code GARMIN GPS receiver, the positioning accuracy less than 5 m (1σ) is achieved.
暂无评论