Robust Gaussian Mixture Model for Mobile Robots' Vision-based Kinematical Localization

• Main Authors: CHENG Chuanqi, HAO Xiangyang, LI Jiansheng, HU Peng, ZHANG Xu
• Format: Article
• Language: zho
• Published: Surveying and Mapping Press 2018-11-01
• Series: Acta Geodaetica et Cartographica Sinica
• Subjects: vision-based localization; graph optimization; dynamic landmarks; covariance inflation; expectation maximization
• Online Access: http://html.rhhz.net/CHXB/html/2018-11-1446.htm

In dynamic environments,the moving landmarks can make the accuracy of traditional vision-based localization worse or even failure.To solve this problem,a robust Gaussian mixture model for vision-based localization with dynamic landmarks is proposed.The motion index is added to the traditional graph-based vision-based localization model to describe landmarks' moving probability,changing the classic Gaussian model to Gaussian mixture model,which can reduce the influence of moving landmarks for optimization results.To improve the algorithm's robustness to noise,the covariance inflation model is employed in residual equations.The expectation maximization method for solving the Gaussian mixture problem is derived in detail,transforming the problem into classic iterative least square problem.Experimental results demonstrate that in dynamic environments,the proposed algorithm outperforms the traditional method both in absolute accuracy and relative accuracy,while maintains high accuracy in static environments.The proposed method can effectively reduce the influence of the moving landmarks in dynamic environments,which is more suitable for the autonomous localization of mobile robots.