To deal with the problems in line segment matching due to occlusion, fragmentation and inaccurate extraction of line segment endpoints, especially when the criteria of "most similar matching" was taken in "multiple-to-multiple" matching which may lead to fateful true correspondences lost, a line segment matching algorithm based on geometric constraints and 0-1 programming was proposed. Firstly, a candidate matching set was initially computed based on the spatial adjacency after correcting the vedio frames. Secondly, the multiple geometric constraints, such as epipolar constraint, homography matrix and point-to-line adjacency, were employed to remove the false positive correspondences. Then, the matching problem was modeled into a large scale 0-1 programming. Finally, a two-stage method based on grouping strategy was designed to solve this problem, so as to realize the "one to one" exact matching of line segment feature. The experimental results show that, compared with LS (Line Sigature) and LJL (Line-Junction-Line) methods, the propsed method has a similar performance in correct matching ratio but a larger matching amount over 60% and 11%, respectively. The proposed method can fulfill the line segment matching across vedio frames, which lays the foundation for line-based visual SLAM (Simultaneously Localization and Mapping).

For the localization problem in urban areas, where Global Positioning System (GPS) cannot provide the accurate location as its signal can be easily blocked by the high-rise buildings, a visual localization method based on vertical building facades and 2D bulding boundary map was proposed. Firstly, the vertical line features across two views, which are captured with an onboard camera, were matched into pairs. Then, the vertical building facades were reconstructed using the matched vertical line pairs. Finally, a visual localization method, which utilized the reconstructed vertical building facades and 2D building boundary map, was designed under the RANSAC (RANdom Sample Consensus) framework. The proposed localization method can work in real complex urban scenes. The experimental results show that the average localization error is around 3.6m, which can effectively improve the accuracy and robustness of self-localization of mobile robots in urban environments.

There is much research about key agreement focus on the secure communication in the same Key Generation Center (KGC), i.e. all the parameters of different participants should be provided by one KGC. To realize the secure communication between two users in different KGCs, adopting elliptic curve cryptography algorithm, a new two-party ID-based key agreement protocol was proposed. This new scheme realized key agreement protocol in separate networks between two participants. Then the improved Blake-Wilson model was used to prove the security of the scheme. After being analyzed, the new protocol was proved to be secure, efficient and of lower computational cost. Therefore, the new scheme will be compatible with low-power and lightweight devices.

The existing SignCryption with Proxy Re-Encryption (SCPRE) only provides non-repudiation and its security is weaker than that of the adaptive chosen message attack. Since there are two kinds of signcryption texts, the authors respectively provided two definitions for unforgeability: one was first-level signcryption text's unforgeability produced by the delegator and the other was second-level signcryption text's unforgeability produced by the delegatee. A new signcryption with proxy re-encryption scheme was also proposed, of which the first-level signcryption text and the second-level signcryption text all meet the publicly verifiable requirements, and its security can be proved under random oracle model. And the proposed new scheme is secure and effective. Therefore, this scheme is suitable for high security level requirements of the practical application areas.