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LI Mao-hai HONG Bing-rong LUO Rong-hua WEI Zhen-hua 《浙江大学学报(A卷英文版)》2006,7(6):937-944
INTRODUCTION A key prerequisite for a truly autonomous robot is that it can simultaneously localize itself and accu-rately map its surroundings (Kortenkamp et al., 1998), which is known as Simultaneous Localization and Mapping (SLAM), which, when phrased as a state estimation problem, involves a variable number of dimensions. Murphy and Russell (2001) adopted Rao-Blackwellized particle filters (RBPFs) as an effective way for representing alternative hypotheses on robot paths and ass… 相似文献
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SLAM is one of the most important components in robot navigation. A SLAM algorithm based on image sequences captured by a single digital camera is proposed in this paper. By this algorithm, SIFT feature points are selected and matched between image pairs sequentially. After three images have been captured, the environment＇s 3D map and the camera＇s positions are initialized based on matched feature points and intrinsic parameters of the camera. A robust method is applied to estimate the position and orientation of the camera in the forthcoming images. Finally, a robust adaptive bundle adjustment algorithm is adopted to optimize the environment＇s 3D map and the camera＇s positions simultaneously. Results of quantitative and qualitative experiments show that our algorithm can reconstruct the environment and localize the camera accurately and efficiently. 相似文献
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