Invariant EKF Design for Scan Matching-aided Localization

TL;DR

This paper introduces an invariant extended Kalman filter (IEKF) for indoor robot localization using scan matching data from a Kinect sensor, showing improved performance over traditional methods.

Contribution

It develops and validates a novel IEKF-based approach for scan matching-aided localization, demonstrating its advantages over existing filters.

Findings

IEKF outperforms MEKF in localization accuracy

Experimental validation confirms robustness of the IEKF approach

Proposed method effectively fuses sensor data for indoor navigation

Abstract

Localization in indoor environments is a technique which estimates the robot's pose by fusing data from onboard motion sensors with readings of the environment, in our case obtained by scan matching point clouds captured by a low-cost Kinect depth camera. We develop both an Invariant Extended Kalman Filter (IEKF)-based and a Multiplicative Extended Kalman Filter (MEKF)-based solution to this problem. The two designs are successfully validated in experiments and demonstrate the advantage of the IEKF design.