Lecture

Motion Prediction: EKF with Inertial

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In course

ENV-548: Sensor orientation

Determination of spatial orientation (i.e. position, velocity, attitude) via integration of inertial sensors with satellite positioning. Prerequisite for applications related to remote sensing, environmental monitoring, mobile mapping, robotics, space expl ...

Description

This lecture covers the use of Extended Kalman Filter (EKF) for motion prediction in 2D scenarios, combining inertial navigation systems (INS) and GPS data. The instructor explains the perturbation of ordinary differential equations (ODE) for motion prediction, the correction process using GPS, and the integration of inertial sensors. The lecture also delves into the augmentation for time-correlated errors and the evolution of state and error states in the system.

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Instructor

Jan Skaloud

Expertise Satellite positioning, inertial and integrated navigation Sensor orientation and calibration, attitude determination Mobile mapping, airborne laser scanning Adjustment methods, Kalman Filtering Mission Teaching in 3 EPFL sections and 2 faculties, director of ESO lab. Books / Monographs J. Skaloud and M. Cramer, «Data Capture,» in GIS Handbook, W. Kresse and D. Danko, Eds. Springer, 2011. D. Lichti and J. Skaloud, «Registration and Calibration,» in Airborne and terrestrial laser scanning, H.-G. Ma

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