目的目前已有的单目视觉SLAM(simultaneous localization and mapping)系统每次开始运行时都将初始帧而不是绝对位置设置为参考帧,不能在一个固定的坐标系中获得位姿,导致无法重用已有的建图信息,而且在复杂场景中相机容易跟踪失败,需...目的目前已有的单目视觉SLAM(simultaneous localization and mapping)系统每次开始运行时都将初始帧而不是绝对位置设置为参考帧,不能在一个固定的坐标系中获得位姿,导致无法重用已有的建图信息,而且在复杂场景中相机容易跟踪失败,需要当前帧与已有的关键帧非常相似时才能重定位并继续建图。针对这个问题,提出一种具有重新初始化、地图重用与地图恢复能力的视觉SLAM系统。方法首先,加载先验地图,通过ORB(oriented brief)特征匹配SLAM系统当前帧与先验地图关键帧,并结合重定位方法完成SLAM系统的初始化。接着,为了避免丢失地图,建立一种应对SLAM系统跟踪失败的地图保存机制,保存跟踪成功地图,并提出一种自适应快速重新初始化算法,引入灭点检测,自动选择最佳重新初始化策略,保证SLAM系统继续跟踪与建图,建立的地图称为恢复地图。最后,对于跟踪成功地图与恢复地图,采用改进的回环方法获得它们之间的转换关系,并提出一种地图恢复法,减少跟踪成功地图与恢复地图尺度不一带来的误差,确保得到的全局一致地图更加准确。结果在经过加噪处理的KITTI数据集上进行地图恢复融合的测试,实验结果表明,在KITTI00、KITTI02、KITTI05数据集下,本文提出的SLAM系统比ORB-SLAM2系统分别可以多获得39.25%、47.75%、32.46%的地图信息。在EuRo C数据集上的运行结果表明,本文提出的单目视觉SLAM系统不仅在建图精度方面与ORB-SLAM2效果相当,还在跟踪稳定性方面有显著提升。结论本文提出的SLAM系统可以在跟踪失败的情况下有效恢复地图;此外,还可以高效重用SLAM系统已有的建图结果,固定SLAM地图坐标系,提升SLAM系统运行稳定性。展开更多
A novel lossless information hiding algorithm based on wavelet neural network for digital vector maps is introduced. Wavelet coefficients being manipulated are embedded into a vector map, which could be restored by ad...A novel lossless information hiding algorithm based on wavelet neural network for digital vector maps is introduced. Wavelet coefficients being manipulated are embedded into a vector map, which could be restored by adjusting the weights of neurons in the designed neural network. When extracting the watermark extraction, those coefficients would be extracted by wavelet decomposition. With the trained multilayer feed forward neural network, the watermark would be obtained finally by measuring the weights of neurons. Experimental results show that the average error coding rate is only 6% for the proposed scheme and compared with other classical algorithms on the same tests, it is indicated that the proposed algorithm hashigher robustness, better invisibility and less loss on precision.展开更多
文摘A novel lossless information hiding algorithm based on wavelet neural network for digital vector maps is introduced. Wavelet coefficients being manipulated are embedded into a vector map, which could be restored by adjusting the weights of neurons in the designed neural network. When extracting the watermark extraction, those coefficients would be extracted by wavelet decomposition. With the trained multilayer feed forward neural network, the watermark would be obtained finally by measuring the weights of neurons. Experimental results show that the average error coding rate is only 6% for the proposed scheme and compared with other classical algorithms on the same tests, it is indicated that the proposed algorithm hashigher robustness, better invisibility and less loss on precision.
