State Estimation for Legged Robots - Consistent Fusion of Leg Kinematics and IMU
State Estimation for Legged Robots - Consistent Fusion of Leg Kinematics and IMU
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DOI：
10.15607/rss.2012.viii.003
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Michael Blösch;Marco Hutter;M. Höpflinger;Stefan Leutenegger;Christian Gehring;C. Remy;R. Siegwart
中科院分区：
文献类型：
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作者：
Michael Blösch;Marco Hutter;M. Höpflinger;Stefan Leutenegger;Christian Gehring;C. Remy;R. Siegwart
- 本文介绍了一个状态估计框架腿机器人，允许估计的机器人的完整姿态，而不作任何假设的几何结构的环境。这是通过一个可观测性约束扩展卡尔曼滤波器，融合运动学编码器的数据与车载IMU测量。通过将所有立足点的绝对位置纳入滤波器状态，可以制定简单的模型方程，精确地捕捉与间歇性地面接触相关的不确定性。由此产生的滤波器同时估计所有立足点的位置和主体的姿势。在算法公式中，特别注意线性化滤波器的一致性：它保持与非线性系统相同的可观测性，这是精确状态估计的先决条件。所提出的方法是在仿真和实验验证一个实际的四足机器人。
—This paper introduces a state estimation framework for legged robots that allows estimating the full pose of the robot without making any assumptions about the geometrical structure of its environment. This is achieved by means of an Observability Constrained Extended Kalman Filter that fuses kinematic encoder data with on-board IMU measurements. By including the absolute position of all footholds into the filter state, simple model equations can be formulated which accurately capture the uncertainties associated with the intermittent ground contacts. The resulting filter simultaneously estimates the position of all footholds and the pose of the main body. In the algorithmic formulation, special attention is paid to the consistency of the linearized filter: it maintains the same observability properties as the nonlinear system, which is a prerequisite for accurate state estimation. The presented approach is implemented in simulation and validated experimentally on an actual quadrupedal robot.