首页|Findings on Robotics Reported by Investigators at University of Science and Technology China (3-d Lidar Localization Based On Novel Nonlinear Optimization Method for Autonomous Ground Robot)
Findings on Robotics Reported by Investigators at University of Science and Technology China (3-d Lidar Localization Based On Novel Nonlinear Optimization Method for Autonomous Ground Robot)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Robotics. According to news reporting from Hefei, People’s Republic of China, by NewsRx journalists, research stated, “3-D light detection and ranging (LiDAR)-based localization for autonomous ground robot in unknown environment is a critical ability, which has been extensively studied. In this article, we propose a novel nonlinear optimization method to promote the 3-D LiDAR localization performance.” Financial support for this research came from National Natural Science Foundation of China (NSFC). The news correspondents obtained a quote from the research from the University of Science and Technology China, “First, due to that the objective function is the summation of point correspondence matching error items, a novel item quality evaluation criterion is proposed. The quality of each item is directly proportional to the degree of its matching error distance descent during the optimization process. Then, an attention mechanism based on the criterion is proposed, and the objective function is iteratively refined by putting different attention weights on the matching error items. Finally, in the Gauss-Newton optimization framework for LiDAR localization, we point out that the Hessian matrix is essentially the sum of the Hessian matrices of high-quality and low-quality point correspondence subsets. To increase the dominance of the Hessian matrix derived from the high-quality point correspondences, the Hessian matrix of the estimated high-quality point correspondence subset in the last updating step is added to the current Hessian matrix. The proposed LiDAR localization is extensively evaluated on public and custom datasets. The experimental results demonstrate that the proposed mechanisms can effectively promote the LiDAR localization performance.”
HefeiPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningRobotRoboticsUniversity of Science and Technology China
NETL
NSTL
IEEE
