Legged Robot State Estimation in Slippery Environments Using Invariant Extended Kalman Filter with Velocity Update
Sangli Teng,Mark W. Mueller,Koushil Sreenath +2 more
- pp 3104-3110
TLDR
In this article, an invariant Extended Kalman Filter (InEKF) is implemented to fuse inertial and velocity measurements from a tracking camera and leg kinematic constraints.Abstract:
This paper proposes a state estimator for legged robots operating in slippery environments. An Invariant Extended Kalman Filter (InEKF) is implemented to fuse inertial and velocity measurements from a tracking camera and leg kinematic constraints. The misalignment between the camera and the robot-frame is also modeled thus enabling auto-calibration of camera pose. The leg kinematics based velocity measurement is formulated as a right-invariant observation. Nonlinear observability analysis shows that other than the rotation around the gravity vector and the absolute position, all states are observable except for some singular cases. Discrete observability analysis demonstrates that our filter is consistent with the underlying nonlinear system. An online noise parameter tuning method is developed to adapt to the highly time-varying camera measurement noise. The proposed method is experimentally validated on a Cassie bipedal robot walking over slippery terrain. A video for the experiment can be found at https://youtu.be/VIqJL0cUr7s.read more
Citations
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Journal ArticleDOI
STEP: State Estimator for Legged Robots Using a Preintegrated Foot Velocity Factor
TL;DR: In this paper , a state estimator for legged robots, STEP, achieved through a novel preintegrated foot velocity factor, is proposed, where the usual non-slip assumption is not adopted.
Journal ArticleDOI
Invariant Filtering for Legged Humanoid Locomotion on a Dynamic Rigid Surface
Yun Gao,Chen Yuan,Yan Gu +2 more
TL;DR: In this article , an invariant extended Kalman filter was proposed to estimate the robot's pose and velocity during DRS locomotion by using common sensors of legged robots [e.g., inertial measurement units (IMUs), joint encoders, and RDB-D camera].
Journal ArticleDOI
Progress in symmetry preserving robot perception and control through geometry and learning
Maani Ghaffari,Ray Zhang,Minghan Zhu,Chien Erh Lin,Tzu-Yuan Lin,Sangli Teng,Ting-Ting Li,Tianyi Liu,Jingwei Song +8 more
TL;DR: Inspired by existing mathematical tools for studying the symmetry structures of geometric spaces, geometric sensor registration, state estimator, and control methods provide indispensable insights into the problem formulations and generalization of robotics algorithms to challenging unknown environments.
Proceedings ArticleDOI
Invariant Extended Kalman Filtering for Human Motion Estimation with Imperfect Sensor Placement
TL;DR: In this paper , an invariant extended Kalman filter was proposed to produce real-time state estimates and rapid error convergence for the estimation of human body movement even in the presence of sensor misalignment and initial state estimation errors.
Journal ArticleDOI
On Slip Detection for Quadruped Robots
TL;DR: In this paper , a slip detection approach for legged robots is proposed, which is independent of the gait type and the estimation of the position and velocity of the robot in an inertial frame.
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State Estimation for Legged Robots - Consistent Fusion of Leg Kinematics and IMU
Michael Bloesch,Marco Hutter,Mark A. Hoepflinger,Stefan Leutenegger,Christian Gehring,C. D. Remy,Roland Siegwart +6 more
TL;DR: 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 is introduced by means of an Observability Constrained Extended Kalman Filter that fuses kinematic encoder data with on-board IMU measurements.
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