tailieunhanh - Robot Localization and Map Building Part 12

Tham khảo tài liệu 'robot localization and map building part 12', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Visual Odometry and mapping for underwater Autonomous Vehicles 379 1026 frames the kohonen map represents the relevant and noise tolerant descriptors space using a reduced number of nodes. This SOM can be used to locate the robot during the navigation. Frames Keypoints Nodes 324 20353 280 684 35813 345 1026 44903 443 Table 2. building the map with gcs algorithm b Location of robot on the map New frames are captured during the navigation. We use the trained SOM to map locate the robot in the environment. Figure 12 shows the estimated position of a navigation task. In this task the robot crosses three times the position . In this figure we can see the position estimated by both the SOM map blue and only by visual odometry red . In the crossings table III shows the normalized errors of positioning in each of the methods. The reduced error associated with the SOM localization validate the robusteness of topological approach. Visual Odometry SOM Fig. 12. Distance Y generated by ROVFURGII in movement. 380 Robot Localization and Map Building 5. Conclusion This work proposed a new approach to visual odometry and mapping of a underwater robot using only online visual information. This system can be used either in autonomous inspection tasks or in control assistance of robot closed-loop in case of a human remote operator. A set of tests were performed under different underwater conditions. The effectiveness of our proposal was evaluated inside a set of real scenario with different levels of turbidity snow marine non-uniform illumination and noise among others conditions. The results have shown the SIFT advantages in relation to others methods as KLT in reason of its invariance to illumination conditions and perspective transformations. The estimated localization is robust comparing with the vehicle real pose. Considering time performance our proposal can be used to online AUV SLAM even in very extreme sea conditions. The correlations of .