Yang Guihua
ABSTRACT
At present, mobile robots have been more and more widely used, in order to enable mobile robots to achieve autonomous localization and mapping (SLAM), and to solve the problem of cumulative errors caused by long-term movement of robots; The current mainstream filtering method does not solve the accumulated error caused by the mileage meter. In order to correct the accumulated error caused by the mileage meter, this design uses Cartographer algorithm to build the map, and correct the accumulated error of mileage to achieve the accurate positioning of the robot. Taking ROS system as the operating platform, the experimental results show that the mobile robot corrects the accumulated error of mileage very well, and achieves more accurate positioning and better environmental map construction.
- zhu kai, liu huafeng, xia qingyuan. Review of simultaneous positioning and mapping algorithm based on monocular vision [J]. Computer application research, 2018, 35(1).Google Scholar
- D 'alfonso L, Griffo A, Muraca P,Et al. A SLAM algorithm for indoor mobile robot localization using an Extended Kalman filter and A segment based environment mapping[C]// International Conference on Advanced Robotics. IEEE, 2013:1--6.Google Scholar
- Turner R, Rasmussen C e. Model based learning of sigma points in unscented Kalman filtering[J]. Neurocomputing, 2010, 80(2):47--53. (in Chinese)Google Scholar
- wu chengding, yao jianmin, hu hailong. Cartographer 2D SLAM algorithm study [J]. Cable technology, 2008,25(4):20--22.Google Scholar
- Xian jun. Improvement of gaussian fuzzy algorithm and application of image processing [J]. Computer optical disc software and application, 2013(19):129--129.Google Scholar
- Zhang qian. Path planning for robot based on lidar [J]. Laser journal, 2018, 39(5).Google Scholar
- anonymous. Study on optimal robot pose estimation algorithm in SLAM process [J]. Journal of high technology communications, 2018, 28(8):48--54.Google Scholar
- Ding kiliang, shen yunzhong, ou jikun. Global least square linear fitting [J]. Journal of liaoning university of engineering technology (natural science edition), 2010, 29(1):44--47.Google Scholar
- Wang Yaonan, Yu Hongshan, WANG Yao-nan, et al. Progress in the study of synchronous map creation and location of mobile robots in unknown environments [J]. Control theory and application, 2008, 25 (1): 57--65.Google Scholar
- Ningshan, Meng Qingqiang. Simultaneous localization and mapping of mobile robots in unknown environments [J]. Journal of Heilongjiang University of Science and Technology, 2007, 17 (6): 452--455.Google Scholar
- Hu Zunhe. Vision Location and Mapping of Mobile Robots in Unknown Environment [D]. CAAC University, 2017.Google Scholar
- Anonymous. SLAM Research of Tracked Mobile Robot in Unknown Environment [J]. Sensors and Microsystems, 2018, 37 (10): 50--53.Google Scholar
Index Terms
- Simultaneous Positioning And Map Construction of Mobile Robots Based on The Cartographer Algorithm
Recommendations
Map-based navigation in mobile robots
This article reviews map-learning and path-planning strategies within the context of map-based navigation in mobile robots. Concerning map-learning, it distinguishes metric maps from topological maps and describes procedures that help maintain the ...
Map-based navigation in mobile robots
For a robot, an animal, and even for man, to be able to use an internal representation of the spatial layout of its environment to position itself is a very complex task, which raises numerous issues of perception, categorization and motor control that ...
Appearance-Based SLAM for Mobile Robots
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial IntelligenceThis paper reviews new challenges in the area of long-term navigation, and new approaches to environment representation and robots capable of coping with dynamic environments. As a result of this review, we propose an appearancebased simultaneous ...
Comments