Accurate attitude estimation using low-cost sensors is an important capability to enable many robotic applications. In this paper, we present a method based on the concept of correntropy in Kalman filtering to estimate the 3D orientation of a rigid body using a low-cost inertial measurement unit (IMU). We then leverage the proposed attitude estimation framework to develop a LiDAR-Intertial Odometry (LIO) demostrating improved localization accuracy, in particular when the robot undergoes high-rate motions (\\eg, during the exploration of challenging and unstructured terrains). The proposed orientation estimation approach is first validated using a low-cost IMU sensor. Then, we demonstrate the performance of the proposed LIO solution in simulated robotic cave exploration scenarios.


Maximum Correntropy Kalman Filter for Orientation Estimation With Application to Lidar Inertial Odometry

Division / Sponsors: 
Dynamic Systems & Control (DSCD)

Paper Proceedings: https://asmedigitalcollection.asme.org/DSCC/DSCC2020/volume/84270

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