Session
2023 session 5
Location
Weber State University
Start Date
5-8-2023 11:20 AM
Description
Continuous-time estimation using splines on Lie groups has been gaining traction in the literature in recent years due to their ability to incorporate high-frequency sensor data without introducing new optimization parameters. However, evaluating time derivatives and Jacobians of Lie group splines is computationally expensive, limiting their use mainly to offline applications. Motivated by the trajectory planning literature, we develop a new estimation technique that leverages the differential flatness property of many dynamic systems to define the spline in the system's flat output space, which is often Euclidean, and show an example of its use with the unicycle dynamic model. We then show that this new method can achieve similar performance as Lie group spline estimation with significantly less computation time, and validate its use with measurements collected from a differential-drive robot.
Continuous-Time Trajectory Estimation for Differentially Flat Systems
Weber State University
Continuous-time estimation using splines on Lie groups has been gaining traction in the literature in recent years due to their ability to incorporate high-frequency sensor data without introducing new optimization parameters. However, evaluating time derivatives and Jacobians of Lie group splines is computationally expensive, limiting their use mainly to offline applications. Motivated by the trajectory planning literature, we develop a new estimation technique that leverages the differential flatness property of many dynamic systems to define the spline in the system's flat output space, which is often Euclidean, and show an example of its use with the unicycle dynamic model. We then show that this new method can achieve similar performance as Lie group spline estimation with significantly less computation time, and validate its use with measurements collected from a differential-drive robot.