Research on Positioning Accuracy of Mobile Robot in Indoor Environment Based on Improved RTABMAP Algorithm
Author:
Zhou Shijie1,
Li Zelun1,
Lv Zhongliang1ORCID,
Zhou Chuande1,
Wu Pengcheng1,
Zhu Changshuang1,
Liu Wei1
Affiliation:
1. Chongqing University of Science and Technology, Chongqing 401331, China
Abstract
Visual simultaneous localization and mapping is a widely used technology for mobile robots to carry out precise positioning in the environment of GNSS technology failure. However, as the robot moves around indoors, its position accuracy will gradually decrease over time due to common and unavoidable environmental factors. In this paper, we propose an improved method called RTABMAP-VIWO, which is based on RTABMAP. The basic idea is to use an Extended Kalman Filter (EKF) framework for fusion attitude estimates from the wheel odometry and IMU, and provide new prediction values. This helps to reduce the local cumulative error of RTABMAP and make it more accurate. We compare and evaluate three kinds of SLAM methods using both public datasets and real indoor scenes. In the dataset experiments, our proposed method reduces the Root-Mean-Square Error (RMSE) coefficient by 48.1% compared to the RTABMAP, and the coefficient is also reduced by at least 29.4% in the real environment experiments. The results demonstrate that the improved method is feasible. By incorporating the IMU into the RTABMAP method, the trajectory and posture errors of the mobile robot are significantly improved.
Funder
Development Joint Fund of Chongqing Natural Science Foundation
Natural Science Foundation of Chongqing
Innovation and National Natural Science Foundation of China
Science and Technology Research Program of Chongqing Municipal Education Commission
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry