里程计
计算机科学
扩展卡尔曼滤波器
惯性测量装置
全球导航卫星系统应用
计算机视觉
移动机器人
人工智能
均方误差
机器人
同时定位和映射
卡尔曼滤波器
弹道
职位(财务)
数学
全球定位系统
电信
天文
统计
物理
经济
财务
作者
Shijie Zhou,Zelun Li,Zhongliang Lv,Chuande Zhou,Pengcheng Wu,Chenyang Zhu,Wei Liu
出处
期刊:Sensors
[MDPI AG]
日期:2023-11-28
卷期号:23 (23): 9468-9468
摘要
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.
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