We present a real-time monocular simultaneous localization and mapping(SLAM)system with a new distributed structure for multi-UAV collaboration tasks.The system is different from other general SLAM systems in two aspe...We present a real-time monocular simultaneous localization and mapping(SLAM)system with a new distributed structure for multi-UAV collaboration tasks.The system is different from other general SLAM systems in two aspects:First,it does not aim to build a global map,but to estimate the latest relative position between nearby vehicles;Second,there is no centralized structure in the proposed system,and each vehicle owns an individual metric map and an ego-motion estimator to obtain the relative position between its own map and the neighboring vehicles'.To realize the above characteristics in real time,we demonstrate an innovative feature description and matching algorithm to avoid catastrophic expansion of feature point matching workload due to the increased number of UAVs.Based on the hash and principal component analysis,the matching time complexity of this algorithm can be reduced from 0(logN)to 0(1).To evaluate the performance,the algorithm is verified on the acknowledged multi-view stereo benchmark dataset,and excellent results are obtained.Finally,through the simulation and real flight experiments,this improved SLAM system with the proposed algorithm is validated.展开更多
文摘We present a real-time monocular simultaneous localization and mapping(SLAM)system with a new distributed structure for multi-UAV collaboration tasks.The system is different from other general SLAM systems in two aspects:First,it does not aim to build a global map,but to estimate the latest relative position between nearby vehicles;Second,there is no centralized structure in the proposed system,and each vehicle owns an individual metric map and an ego-motion estimator to obtain the relative position between its own map and the neighboring vehicles'.To realize the above characteristics in real time,we demonstrate an innovative feature description and matching algorithm to avoid catastrophic expansion of feature point matching workload due to the increased number of UAVs.Based on the hash and principal component analysis,the matching time complexity of this algorithm can be reduced from 0(logN)to 0(1).To evaluate the performance,the algorithm is verified on the acknowledged multi-view stereo benchmark dataset,and excellent results are obtained.Finally,through the simulation and real flight experiments,this improved SLAM system with the proposed algorithm is validated.
