基于改进的RTABMAP算法,研究移动机器人在室内环境中的定位精度
Shijie Zhou1, Zelun Li1, Zhongliang Lv1
1Chongqing University of Science and Technology, Chongqing 401331, China.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
这项研究介绍了RTABMAP-VIWO,一种增强的视觉同步定位和映射 (VSLAM) 方法. 它通过融合轮子测距和IMU数据,显著减少室内机器人定位错误,提高准确性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 同时定位和绘制 (SLAM)
背景情况:
- 视觉SLAM对于移动机器人定位在GNSS失败的情况下至关重要.
- 室内机器人导航由于环境因素而遭受累积位置错误.
- 像RTABMAP这样的现有方法的准确性会随着时间的推移而下降.
研究的目的:
- 为了提高移动机器人的Visual SLAM的准确性和减少累积错误.
- 通过整合惯性测量单元 (IMU) 数据来增强RTABMAP方法.
- 评估拟议的RTABMAP-VIWO方法与现有的SLAM技术的性能.
主要方法:
- 根据RTABMAP框架提出的RTABMAP-VIWO方法.
- 使用扩展卡尔曼波器 (EKF) 将轮距和IMU态度估计结合起来.
- 提供了新的预测值以减轻本地累积错误.
主要成果:
- 与公开数据集上的RTABMAP相比,RTABMAP-VIWO减少了48.1%的根平均平方误差 (RMSE).
- 在现实室内实验中,RMSE至少减少了29.4%.
- 在轨迹和姿势错误减少方面取得了显著的改善.
结论:
- RTABMAP-VIWO方法有效地减少了室内移动机器人定位中的累积错误.
- 将IMU数据纳入RTABMAP可以显著提高定位准确性.
- 拟议的方法为VSLAM在具有挑战性的室内环境中提供了可行和更准确的解决方案.
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