A reliable pose estimator robust to environmental disturbances is desirable for mobile robots. To this end, inertial measurement units (IMUs) play an important role because they can perceive the full motion state of the vehicle independently. However, it suffers from accumulative error due to inherent noise and bias instability, especially for low-cost sensors. In our previous studies on Wheel-INS \cite{niu2021, wu2021}, we proposed to limit the error drift of the pure inertial navigation system (INS) by mounting an IMU to the wheel of the robot to take advantage of rotation modulation. However, Wheel-INS still drifted over a long period of time due to the lack of external correction signals. In this letter, we propose to exploit the environmental perception ability of Wheel-INS to achieve simultaneous localization and mapping (SLAM) with only one IMU. To be specific, we use the road bank angles (mirrored by the robot roll angles estimated by Wheel-INS) as terrain features to enable the loop closure with a Rao-Blackwellized particle filter. The road bank angle is sampled and stored according to the robot position in the grid maps maintained by the particles. The weights of the particles are updated according to the difference between the currently estimated roll sequence and the terrain map. Field experiments suggest the feasibility of the idea to perform SLAM in Wheel-INS using the robot roll angle estimates. In addition, the positioning accuracy is improved significantly (more than 30\%) over Wheel-INS. The source code of our implementation is publicly available (https://github.com/i2Nav-WHU/Wheel-SLAM).
翻译:对于移动机器人来说,对于环境扰动来说,可靠的表面估计值是可靠的,对于环境扰动是可取的。为此,惯性测量单位(IMU)起着重要作用,因为它们能够独立地看到该飞行器的完全运动状态。然而,由于固有的噪音和偏差不稳定,特别是低成本传感器的噪音和偏差不稳定,它受到累积性错误的影响。在我们以前对轮式导航系统的研究中,我们建议限制纯惯性导航系统(INS)的误差漂移,办法是将IMU安装到机器人的轮式轮式轮式轮式轮式轮式调整。然而,轮式惯性测量单位(IMU)仍然在很长一段时间内漂移动,因为缺乏外部校正信号。在本信里,我们提议利用轮式导航系统的环境观察能力实现同步本地化和绘图(SLAM) 。我们用路轮式滚动滚动滚动滚动滚动滚动螺旋角度角度作为地形特征(由轮式滚动螺旋角度估计的螺旋角度) 地面特征(由轮式滚动螺旋轴角度角度估计),在拉动式粒子粒过滤式粒过滤式粒过滤式的深度中进行定位过滤定位/轨道测量测测测测测测算。