吉林大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (3): 909-918.doi: 10.13229/j.cnki.jdxbgxb20170146
林金花1, 王延杰2, 王璐1, 姚禹3
LIN Jin-hua1, WANG Yan-jie2, WANG Lu1, YAO Yu3
摘要: 针对传统三维重建算法存在的漂移问题,提出了一种端到端的在线大规模三维场景重建算法。首先,使用一种在线估计策略来鲁棒地确定相机的旋转姿态,同时构建层次优化框架用于融合深度数据的输入。然后,依据相机的全局估计姿态对每一帧的信息进行优化,解除了算法对目标跟踪时间的限制,完成了对帧间关系对象的实时跟踪。试验结果表明:本文算法的平均重建时间为399 ms,平均估计迭代最低点(ICP)次数为20,完成每帧变换的时间为100 ms;系统对大规模场景的重建具有鲁棒性,且实时性较好,是一种具有对应关系稀疏特性、结构信息稠密特性和相机光照一致特性的实时三维重建算法。
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